Tag: deep learning

Binary-Class Image Classification Model for Ultrasound Breast Cancer Classification Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Ultrasound Breast Cancer Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 9,000 images of ultrasound images. The research team collected these images to investigate the machine learning model’s ability to distinguish benign and malignant breast cancer images. The research team also augmented the images by rotating and sharpening them to produce a sufficient amount of images.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 99.57% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 98.33%.

CONCLUSION: In this iteration, the TensorFlow Xception CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Ultrasound Breast Images for Breast Cancer

Dataset Reference: https://www.kaggle.com/datasets/vuppalaadithyasairam/ultrasound-breast-images-for-breast-cancer

One source of potential performance benchmarks: https://www.kaggle.com/datasets/vuppalaadithyasairam/ultrasound-breast-images-for-breast-cancer/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Tomato Leaf Disease Using TensorFlow Take 5

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Tomato Leaf Disease Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 20,000 images of tomato leaves with ten diseases and one healthy class. The research team collected these images to investigate the possibilities of developing a lightweight model that can predict tomato leaf disease on a mobile app.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 97.54% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 96.75%.

CONCLUSION: In this iteration, the TensorFlow InceptionV3 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Tomato Leaves Dataset

Dataset Reference: https://www.kaggle.com/datasets/ashishmotwani/tomato

One source of potential performance benchmarks: https://www.kaggle.com/datasets/ashishmotwani/tomato/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Tomato Leaf Disease Using TensorFlow Take 4

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Tomato Leaf Disease Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 20,000 images of tomato leaves with ten diseases and one healthy class. The research team collected these images to investigate the possibilities of developing a lightweight model that can predict tomato leaf disease on a mobile app.

ANALYSIS: The NASNetMobile model’s performance achieved an accuracy score of 97.49% after ten epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 84.33%.

CONCLUSION: In this iteration, the TensorFlow NASNetMobile CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Tomato Leaves Dataset

Dataset Reference: https://www.kaggle.com/datasets/ashishmotwani/tomato

One source of potential performance benchmarks: https://www.kaggle.com/datasets/ashishmotwani/tomato/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Tomato Leaf Disease Using TensorFlow Take 3

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Tomato Leaf Disease Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 20,000 images of tomato leaves with ten diseases and one healthy class. The research team collected these images to investigate the possibilities of developing a lightweight model that can predict tomato leaf disease on a mobile app.

ANALYSIS: The ResNet50V2 model’s performance achieved an accuracy score of 96.48% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 96.56%.

CONCLUSION: In this iteration, the TensorFlow ResNet50V2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Tomato Leaves Dataset

Dataset Reference: https://www.kaggle.com/datasets/ashishmotwani/tomato

One source of potential performance benchmarks: https://www.kaggle.com/datasets/ashishmotwani/tomato/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Tomato Leaf Disease Using TensorFlow Take 2

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Tomato Leaf Disease Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 20,000 images of tomato leaves with ten diseases and one healthy class. The research team collected these images to investigate the possibilities of developing a lightweight model that can predict tomato leaf disease on a mobile app.

ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 96.74% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.96%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2M CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Tomato Leaves Dataset

Dataset Reference: https://www.kaggle.com/datasets/ashishmotwani/tomato

One source of potential performance benchmarks: https://www.kaggle.com/datasets/ashishmotwani/tomato/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Tomato Leaf Disease Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Tomato Leaf Disease Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 20,000 images of tomato leaves with ten diseases and one healthy class. The research team collected these images to investigate the possibilities of developing a lightweight model that can predict tomato leaf disease on a mobile app.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 97.97% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 97.74%.

CONCLUSION: In this iteration, the TensorFlow Xception CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Tomato Leaves Dataset

Dataset Reference: https://www.kaggle.com/datasets/ashishmotwani/tomato

One source of potential performance benchmarks: https://www.kaggle.com/datasets/ashishmotwani/tomato/code

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Car vs. Bike Classification Using TensorFlow Take 5

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 98.56% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 99.17%.

CONCLUSION: In this iteration, the TensorFlow InceptionV3 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Car vs. Bike Classification Dataset

Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Car vs. Bike Classification Using TensorFlow Take 4

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.

ANALYSIS: The NASNetMobile model’s performance achieved an accuracy score of 98.53% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.33%.

CONCLUSION: In this iteration, the TensorFlow NASNetMobile CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Car vs. Bike Classification Dataset

Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code

The HTML formatted report can be found here on GitHub.