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Is your feature request related to a problem? Please describe.
The main purpose of this project is to classify between X-Ray Images to find X-rays for COVID-19 from the dataset (mentioned below) using various image detection/recognition models and comparing their accuracy.
Describe the solution you'd like along with reference dataset.
This project involves the comparative analysis of Five Keras image detection models, namely MobileNetV2 , VGG16 , InceptionV3 , DenseNet121 and Xception applied to a specific dataset. The dataset consists of annotated images related to a particular domain, and the objectives include training and evaluating these models to compare their accuracy scores and performance metrics. Additionally, exploratory data analysis (EDA) techniques are employed to understand the dataset's characteristics, explore class distributions, detect imbalances, and identify areas for potential improvement. The methodology encompasses data preparation, model training, evaluation, comparative analysis of accuracy and performance metrics, and visualization of EDA insights.
Code of Conduct
I agree to follow this project's Code of Conduct
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Is your feature request related to a problem? Please describe.
The main purpose of this project is to classify between X-Ray Images to find X-rays for COVID-19 from the dataset (mentioned below) using various image detection/recognition models and comparing their accuracy.
Describe the solution you'd like along with reference dataset.
The link to the dataset is given below :-
Link :- https://www.kaggle.com/datasets/pranavraikokte/covid19-image-dataset
This project involves the comparative analysis of Five Keras image detection models, namely MobileNetV2 , VGG16 , InceptionV3 , DenseNet121 and Xception applied to a specific dataset. The dataset consists of annotated images related to a particular domain, and the objectives include training and evaluating these models to compare their accuracy scores and performance metrics. Additionally, exploratory data analysis (EDA) techniques are employed to understand the dataset's characteristics, explore class distributions, detect imbalances, and identify areas for potential improvement. The methodology encompasses data preparation, model training, evaluation, comparative analysis of accuracy and performance metrics, and visualization of EDA insights.
Code of Conduct
The text was updated successfully, but these errors were encountered: