Category Archives: Machine Learning Projects With Source Code

Ai Black and white image colorization with OpenCV Project Free Download

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Image colorization is an intriguing task in the field of computer vision that involves adding color to black and white images. This process transforms historical photographs, enhances low-quality video footage, and brings new life to vintage images. The advent of deep learning, particularly Convolutional Neural Networks (CNNs), has significantly improved the accuracy and realism of automated colorization.

In traditional image processing, colorization required manual effort and expertise, making it a time-consuming and labor-intensive task. However, with the development of AI and deep learning, we now have models that can learn from large datasets of color images and predict the appropriate colors for grayscale images. This not only saves time but also produces remarkably realistic results.

OpenCV (Open Source Computer Vision Library) is a powerful tool for computer vision and image processing. It provides a wide range of functions for manipulating images, making it an excellent choice for implementing AI-based image colorization. By leveraging OpenCV along with deep learning models, we can automate the process of colorizing black and white images with impressive accuracy.

This report delves into the methodology and implementation of AI-based black and white image colorization using OpenCV. We will discuss the conversion of images to different color spaces, the training of neural networks to predict color channels, and the application of these models to achieve vibrant and realistic colorization of grayscale images.

Technology Overview

Image colorization using AI and OpenCV is a fascinating blend of deep learning and computer vision technologies. Here's an overview of the key technologies and concepts involved:

  1. Deep Learning and Convolutional Neural Networks (CNNs):
    • Deep learning is a subset of machine learning that uses neural networks with many layers to model complex patterns in data.
    • CNNs are a type of deep learning model particularly effective for image processing tasks. They consist of layers that automatically and adaptively learn spatial hierarchies of features from input images.
  2. Color Space Conversion:
    • Images are typically processed in RGB color space but for colorization, conversion to Lab color space is common.
    • In Lab color space, the L channel represents lightness, while the a and b channels represent color information (chromaticity). This separation makes it easier for the model to predict color components.
  3. Training the CNN Model:
    • The model is trained on a large dataset of color images, learning to predict the a and b channels given the L channel.
    • Training involves feeding the network pairs of grayscale (L channel) and color (a and b channels) images, and optimizing the network to minimize the difference between predicted and actual color channels.
  4. Implementation with OpenCV:
    • OpenCV is a widely-used library in computer vision for image manipulation and processing. It provides tools for tasks like loading images, converting color spaces, and applying transformations.
    • OpenCV's dnn module can be used to load and run pre-trained CNN models, making it possible to integrate deep learning models into applications for tasks like colorization.
  5. Application Process:
    • Load the grayscale image.
    • Convert the image to Lab color space.
    • Use the trained CNN model to predict the a and b channels.
    • Combine the predicted a and b channels with the original L channel.
    • Convert the image back to RGB color space to get the final colorized image.

Benefits and Challenges

Benefits:

  • Automates the colorization process, saving time and effort.
  • Produces realistic and high-quality colorized images.
  • Can be applied to various fields like film restoration, historical photo enhancement, and more.

Challenges:

  • Requires a large and diverse dataset for training to achieve good results.
  • May struggle with complex images where the grayscale cues alone are insufficient to infer accurate colors.
  • Computationally intensive, requiring powerful hardware for both training and inference.

This combination of deep learning and computer vision techniques has opened up new possibilities in image colorization, making it more accessible and effective. Would you like to explore any of these technologies in more detail?

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Lung Cancer Detection Using CNN Project with Source Code

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Lung cancer is one of the most prevalent and deadly forms of cancer worldwide. According to the World Health Organization (WHO), lung cancer accounts for approximately 2.1 million new cases annually and is responsible for 1.8 million deaths. The high mortality rate is largely due to the fact that lung cancer is often detected at an advanced stage when treatment options are limited, and survival rates are low. Early detection of lung cancer significantly improves the chances of successful treatment and long-term survival. However, traditional diagnostic methods, such as X-rays, CT scans, and biopsies, are time-consuming, require significant expertise, and are not always effective at detecting early-stage tumors.

This project aims to detect lung cancer using a Convolutional Neural Network (CNN) model deployed with Flask. It includes a Jupyter notebook (lung_cancer_detection.ipynb) for model training and a Flask app (app.py) for making predictions. Additionally, an HTML template (index.html) is provided for the web interface.

Data Preprocessing

  • Data Acquisition: The CT-Scan  images and their labels were obtained from this Kaggle dataset. It provides a diverse set of chest cancer images, crucial for training a robust model.

  • Data Augmentation: To enhance the model's ability to generalize and to mitigate overfitting, I used TensorFlow's ImageDataGenerator. This tool allowed for augmenting the images in various ways (like rotation, zoom, flip) to artificially expand the dataset.

  • Normalization and Resizing: Each image was resized to a standard dimension and normalized to ensure uniformity in the input data, which is important for effective training of the CNN.

  • Train-Test Split: The dataset was split into training and test sets. The training set is used to train the model, while the test set helps in evaluating its performance on unseen data.

Model Structure

The CNN model used in this project is a sequential model composed of several Convolution2D and MaxPooling2D layers, carefully structured for effective image classification:

  • Convolution Layers: The model includes multiple Conv2D layers with 128 and 256 filters. These layers are responsible for extracting features from the images. The first two convolution layers have 128 filters each, followed by another set of two with the same number. The next four convolution layers have 256 filters each.

  • Pooling Layers: MaxPooling2D layers are used after certain convolution layers to reduce the spatial dimensions (width and height) of the output volume, helping to reduce the number of parameters and computation in the network.

  • Output Layer: The final layer of the model is a Dense layer with neurons equal to the number of classes, using a 'softmax' activation function for multi-class classification. This allows the model to output a probability distribution over the classes.

  • Optimization and Loss Function: The model is compiled with the Adamax optimizer and categorical cross-entropy loss function. This combination is chosen for effective learning and generalization in multi-class classification tasks.

  • Parameters and Size: The total number of parameters in the model is 3,763,940 (14.36 MB). All these parameters are trainable, ensuring that the model can learn complex patterns in the data.

This structure is designed to effectively capture the intricate patterns in CT-Scan images, leading to accurate classification of Lung Cancer.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.8, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Brain Tumour Detection Project Using Deep Learning From MRI Image

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Brain tumors are one of the most critical and life-threatening conditions affecting the human brain. Early and accurate detection of brain tumors is crucial for effective treatment planning and improving patient outcomes. However, manual detection and diagnosis from MRI (Magnetic Resonance Imaging) scans can be time-consuming, prone to human error, and require expert radiologists.

This project focuses on automating the process of brain tumor detection using deep learning techniques. By leveraging the power of Convolutional Neural Networks (CNNs), we aim to create a model that can accurately classify MRI images into tumor and non-tumor categories. The trained model is then integrated into a user-friendly web application developed using Flask, allowing healthcare professionals and researchers to easily upload MRI images and receive real-time predictions.

Data Preprocessing

  • Data Acquisition: The MRI images and their labels were obtained from this Kaggle dataset. It provides a diverse set of brain images, crucial for training a robust model.

  • Data Augmentation: To enhance the model's ability to generalize and to mitigate overfitting, I used TensorFlow's ImageDataGenerator. This tool allowed for augmenting the images in various ways (like rotation, zoom, flip) to artificially expand the dataset.

  • Normalization and Resizing: Each image was resized to a standard dimension and normalized to ensure uniformity in the input data, which is important for effective training of the CNN.

  • Train-Test Split: The dataset was split into training and test sets. The training set is used to train the model, while the test set helps in evaluating its performance on unseen data.

Model Structure

The CNN model used in this project is a sequential model composed of several Convolution2D and MaxPooling2D layers, carefully structured for effective image classification:

  • Convolution Layers: The model includes multiple Conv2D layers with 128 and 256 filters. These layers are responsible for extracting features from the images. The first two convolution layers have 128 filters each, followed by another set of two with the same number. The next four convolution layers have 256 filters each.

  • Pooling Layers: MaxPooling2D layers are used after certain convolution layers to reduce the spatial dimensions (width and height) of the output volume, helping to reduce the number of parameters and computation in the network.

  • Output Layer: The final layer of the model is a Dense layer with neurons equal to the number of classes, using a 'softmax' activation function for multi-class classification. This allows the model to output a probability distribution over the classes.

  • Optimization and Loss Function: The model is compiled with the Adamax optimizer and categorical cross-entropy loss function. This combination is chosen for effective learning and generalization in multi-class classification tasks.

  • Parameters and Size: The total number of parameters in the model is 3,763,940 (14.36 MB). All these parameters are trainable, ensuring that the model can learn complex patterns in the data.

This structure is designed to effectively capture the intricate patterns in MRI brain images, leading to accurate classification of brain tumors.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.8, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Buy Now Source Code ₹1501

Sleep Disorder Prediction Using Machine Learning Project

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The purpose of this project is to analyze factors that affect sleep health and predict which sleep disorder a person may have based on various factors such as Sleep Quality, Stress Level, BMI Index, Blood Pressure, etc. The dataset used for this project was sourced from Kaggle. The project involved data preprocessing to fill null values, exploratory data analysis (EDA), and the implementation of machine learning models to make predictions.

Key Observations

  1. Individuals aged over 43 years are more likely to experience sleep disorders.
  2. On average, females tend to have better sleep quality compared to males.
  3. Engineers generally have better sleep quality, while Sales Representatives have poorer sleep quality.
  4. Higher stress levels are associated with an increased likelihood of sleep disorders.
  5. Individuals with sleep disorders have lower sleep quality ratings compared to those without disorders.
  6. People in the Obese and Overweight BMI categories tend to experience more sleep disorders.
  7. Individuals who sleep more than 7 hours have a significantly lower chance of having a sleep disorder.

Models Used

Three machine learning models were used:

  1. Logistic Classification
  2. K-Nearest Neighbors (KNN) Classifier
  3. Random Forest Classifier

The same training and testing sets were used for all models.

Model Performance

  • Random Forest Classifier: Best performing model with an accuracy of 89%.
    • Accuracy: 89%
    • Recall: 89%
    • Precision: 90%
    • F1-Score: 89%
  • KNN Classifier: Performed well but not as good as Random Forest.
  • Logistic Classification: Achieved 86% accuracy, which was the lowest among the three models.

Important Features

Based on the Random Forest Classifier, the top three important features for detecting sleep disorders are:

  1. Blood Pressure
  2. BMI Category
  3. Age

Conclusion

 

The Random Forest Classifier was identified as the best model for predicting sleep disorders in the given dataset, achieving an accuracy of 89%. The top three contributing features to the prediction are Blood Pressure, BMI Category, and Age.

Dataset

The dataset for this project was obtained from Kaggle.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.8, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Buy Now Source Code ₹1001

 

Oral Cancer Detection Using Image Processing with Source code

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Oral cancer is cancer that develops in the tissues of the mouth or throat. It belongs to a larger group of cancers called head and neck cancers. Most develop in the squamous cells found in your mouth, tongue, and lips.

Oral cancer is a major health problem worldwide accounting for 177,384 deaths in 2018 and is most prevalent in low- and middle-income countries. Enabling automation in the identification of oral cancer can lead to the prevention and early diagnostic of disease. Therefore, regular oral check-ups are very important. The focus of transfer learning is to enhance the performance of target learners on target domains by inheriting knowledge from various conceptually related source domain. This project implies a novel approach for the early diagnosis and detection of one of the leading diseases, cancer in most sensory body organ i.e., mouth. In addition to this, deep neural networks are used to build automated systems, where complex patterns are derived to track with this difficult task. Various Transfer Learning architectures has been improvised and comparative analysis has been derived to focus the best learning rate. Further analysis is reported in relation to the classification of the referral decision. Our introductory results shows that deep learning has the power to tackle this challenging task.

In the enlargement of cells that provide with harmed nearby tissues, oral cancer has been documented to be intractable [1]. Oral cancer can be detected in the early stages of mouth cancer, known as ulcers, by a tiny number of sterile cells in the oral tissue. When it comes to metabolism, dead cells can be found in remote places of the geographical region or inside the body. There are several types of cancer, however 90 percent of crab cells are classified as oral cancer [2]. Biological models, as well as clinical forms of related and lesion-free tumour models, can be detected in various parts of the body using appearance models and stereotypes that do not require staining. Machine learning techniques were utilised to predict several biological models for oral cancer, which were used to classify non-cancerous and cancerous samples, which were then analysed for the oral cancer stage [3]. The predictor will use three justification test kits as well as different stages of cancer to determine the accuracy of the interaction. Sampling with sample justification can predict different tumour volumes and the emergence of ulcers in the tissues, assisting in the prediction of different stages of oral cancer [4].

Without the need of any special instruments, the oral cavity can be easily viewed. During clinical practise, specialists use visual inspection to make suspected diagnoses of oral cancer based on their own knowledge and experience with the visual appearances of malignant tumours [5,6]. Oral cancer lesions typically appear as white patches followed by red patches, or as mixed white-red patches in rare cases. The mucosal surface frequently becomes progressively uneven, grainy, and ulcerated [7,8]. Non-specialist medical workers, however, sometimes misinterpret these visual patterns as indicators of ulceration or other types of oral mucous membrane illnesses [8]. For the detection of oral cancer, there is no established vision based technique. Oral cancer is diagnosed through oral biopsy, which takes a long time and is not always available in primary care or community settings, especially in developing countries [9, 10]. As a result, Oral Cancer patiens are frequently unable to acquire prompt diagnosis and referrals [11,12].

There is a lot of evidence that Deep learning algorithms can match, and in some cases outperform, human experts when it comes to identifying minute or miniscule visual patterns from photographs,[13], classifying skin lesions, [14], detecting diabetic retinopathy, [15], and identifying facial phenotypes of genetic disorders [16]. These findings lead us to anticipate that deep learning could capture fine-grained aspects of oral cancer lesions, which would be useful in the early diagnosis of the disease.

The concept of transfer learning is founded on the premise that when knowledge or information from a related domain is transferred to it, it improves an idea in that domain. Consider the case of two people who want to learn to play the flute. One of the participants has no prior musical experience, while the other has a strong understanding of music as a result of playing the sitar. By applying previously learned music knowledge to the process of learning to play the flute, a person with a good music background will be able to learn the flute more quickly [17].

A deep learning system was built using photographic images for entirely automated oral cancer detection when it was assumed that deep neural networks could quickly identify certain visual patterns of oral cancer just like any human expert. On both internal and external validation datasets, we calculated algorithmic performance and compared the model to the average result of seven oral cancer specialists on a clinical validation dataset. Our findings demonstrated that oral cancer lesions have discriminative visual patterns that can be discovered using a deep learning algorithm. The potential to identify oral cancer at the point of care in a less expensive, non-invasive, and effective method has substantial clinical implications.

Datasets Download Link - Kaggle 

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.8, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Big Mart Sales Prediction using Machine Learning Web App

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Big Mart is a retail chain that operates numerous stores across various locations. Predicting sales can help in optimizing inventory, improving sales strategies, and enhancing overall profitability. This project aims to build a predictive model using machine learning techniques to forecast the sales of products in different stores.

Objective

The main objective of this project is to predict the sales of products in different Big Mart outlets based on historical sales data and product attributes.

Data Collection and Preprocessing

Data Description

The dataset used in this project consists of various attributes of products and historical sales data. The key features include:

Datasets Link : - kaggle

  1. Item Identifier
  2. Item Weight
  3. Item Fat Content
  4. Item Visibility
  5. Item Type
  6. Item MRP
  7. Outlet Identifier
  8. Outlet Establishment Year
  9. Outlet Size
  10. Outlet Location Type
  11. Outlet Type
  12. Item Outlet Sales (Target Variable)

Model Building

Model Selection

Several machine learning algorithms were considered, including:

  • Linear Regression
  • Decision Tree Regressor
  • Random Forest Regressor
  • Gradient Boosting Regressor

Model Training

The dataset was split into training and testing sets. Hyperparameter tuning was performed using GridSearchCV to find the best parameters for each model.

Model Evaluation

Models were evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) score.

Installation Steps :-

  1. Install Python 3.8
  2. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  3. Finally run cmd - python app.py

Download Link

Drug Recommendation System based on Sentiment Analysis of Drug Reviews

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The objective of this project is to develop a Drug Recommendation System that leverages sentiment analysis of drug reviews to provide personalized recommendations. This system aims to help users find suitable drugs based on the experiences and feedback of other users with similar conditions. The project involves data collection, preprocessing, sentiment analysis, recommendation generation, and deployment using Flask.

This Project is based on sentiment analysis of the drug whether the drug should be given for patients, it is advisable or not to the patients. This project is implemented using Natural Language processing using a bag of words model and other techniques like vectorization to analyze the drug reviews

Data Collection

Objective

The first step involves gathering a comprehensive dataset containing drug reviews to analyze user sentiments and derive recommendations.

Sources

  • Online Platforms: Websites such as Drugs.com and user forums and Kaggle .
  • Existing Datasets: Publicly available datasets like the UCI Drug Review dataset.

Content

The dataset should include:

  • Review Text: The content of the user's review.
  • Drug Name: The name of the drug being reviewed.
  • Condition: The medical condition the drug is intended to treat.
  • User Ratings: Numerical ratings provided by the users.
  • Metadata: Additional information such as review date, user demographics, etc.

Methods

Collaborative Filtering

  • Leverage user reviews and ratings to find similar users and recommend drugs.

Content-Based Filtering

  • Recommend drugs similar to those the user has positively reviewed based on drug features and user preferences.

Hybrid Approaches

  • Combine collaborative and content-based methods for more accurate recommendations.

Installation Steps :-

  1. Install Python 3.7
  2. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  3. Finally run cmd - python app.py

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Air Quality Index Prediction Using PM 2.5 value Machine Learning

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India is one of the higher air pollution country. Generally, air pollution is assessed by PM value or air quality index value. For my further analysis, I have selected PM-2.5 value to determine the air quality prediction and India-Bangalore region. Also, the data was collected through web scraping with the help of Beautiful Soup.

Data Collection

  • Air quality data was collected from the "http://en.tutiempo.net/climate". So, here I selected the India- Bangalore'sregion & collected the independent features such as Average annual temperature(AT), Annual average maximum temperature(TM), Average annual minimum temperature(Tm), Rain or snow precipitation total annual(PP), Annual average wind speed(V), Number of days with rain(RA), Number of days with snow(SN) and dependent feature as PM 2.5 values has been colected from the "dhewdhjwdhjw"

  • The dataset used can be downloaded Here from the 2013 to 2018.

Technologies Used :

  1. IDE - Pycharm
  2. Linear Regression Model
  3. Ridge and Lasso Regression
  4. Support vector regressor(SVR)
  5. Extra tree regressor
  6. Decission tree regressor
  7. Google Colab - Trained ML model
  8. Flask- Rest API

Installation Step : -

  1. python 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Download Link 

Predict the Earlier Stages of Alzheimer’s disease Machine Learning

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The objective of this project is to develop a predictive model that can identify the early stages of Alzheimer’s disease (AD). Early diagnosis of AD can significantly improve the effectiveness of treatment and management strategies, potentially slowing the progression of the disease. This project will leverage machine learning techniques on various data sources, such as medical imaging, genetic data, and cognitive test results, to create an accurate and reliable prediction system.

Background and Motivation

Alzheimer's disease is a progressive neurodegenerative disorder that affects millions worldwide, leading to memory loss, cognitive decline, and ultimately loss of independence. Early diagnosis is crucial but challenging due to the subtlety of initial symptoms. Current diagnostic methods rely heavily on clinical assessment and are often made at advanced stages. By predicting the onset of AD in its early stages, we can provide better intervention options, potentially improving the quality of life for patients and reducing healthcare costs.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.7, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Download Link