Detele README.md

README.md

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-# Pneumonia Detection Model
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-## Model Description
-This deep learning model is trained to detect signs of pneumonia from chest X-ray images. It utilizes the power of MobileNetV3 as a backbone for feature extraction, coupled with additional convolutional layers for improved accuracy.
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-## Purpose
-The model is designed to assist healthcare professionals in screening patients for pneumonia, potentially speeding up diagnosis and treatment planning.
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-## Training Data Description
-The model was trained on a comprehensive dataset of chest X-ray images sourced from [Kaggle's Pneumonia Dataset](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia). The dataset comprises frontal-view X-ray images of pediatric patients, categorized into pneumonia and normal health status.
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-## Model Architecture
-The architecture leverages a pre-trained MobileNetV3 model with the top layer removed. Custom convolutional layers are added to capture the nuances of X-ray images specific to pneumonia.
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-## Training Procedures
-The model was trained using the Adam optimizer with a learning rate of 1e-4. We employed early stopping mechanisms to prevent overfitting, and the model's performance was validated against a separate validation set comprising 20% of the training data.
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-## Performance/Benchmarks
-The model achieved an accuracy of 93.22% on the training set and 91.48% on the validation set. Further details on the model's performance, including precision, recall, and F1 score, are provided within the model's training logs.
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