Revised README file

README.md

@@ -40,5 +40,5 @@
 - CNN Deep Learning for Medical Imaging:
  - **Title:** ["Comparative Analysis of CNN Deep Learning Models for X-ray Illness Classification"](./Neural%20Network%20Models/X-Ray%20Deep%20Learning%20Classificaton%20Models.html)
  - Focus: Deep Learning for Image Classification
- Technology Used: Python, Keras, CNNs, Transfer Learning, ImageDataGenerator, flow_from_directory, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
- - Contents: Comparing the performance of 8 CNN deep learning models on X-ray images from three classes (COVID-19, viral pneumonia, and healthy). These include transfer learning models (e.g., InceptionV3), and various techniques to improve model generalization and help avoid overfitting (e.g., dropout, batch normalization, early stopping, data augmentation, L1 and L2 regularization, fire modules, and ways of using deep networks effectively). I also demonstrate best practices for structuring filters/kernels, channels, layers, activation functions, pooling, convolutional blocks, and other model components for optimal performance. Metrics include confusion matrixes, accuracy, precision, recall, F1-score, ROC curve, and AUC. Analysis of non-augmented vs. augmented data models with specific augmentation techniques are shown. Architectures and training strategies for each model are detailed. 
+ - Technology Used: Python, Keras, CNNs, Transfer Learning, ImageDataGenerator, flow_from_directory, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
+ - Contents: Comparing the performance of 8 CNN deep learning models on X-ray images from three classes (COVID-19, viral pneumonia, and healthy). These include transfer learning models (e.g., InceptionV3), and various techniques to improve model generalization and help avoid overfitting (e.g., dropout, batch normalization, early stopping, data augmentation, L1 and L2 regularization, fire modules, and ways of using deep networks effectively). I also demonstrate best practices for structuring filters/kernels, channels, layers, activation functions, pooling, convolutional blocks, and other model components for optimal performance. Metrics include confusion matrixes, accuracy, precision, recall, F1-score, ROC curve, and AUC. Analysis of non-augmented vs. augmented data models with specific augmentation techniques are shown. Architectures and training strategies for each model are detailed.