Leveraging Hybrid CNN Architecture for Accurate Detection of Lung Diseases

Deep learning has transformed the analysis of medical images and has provided a potential solution to the problem of accurate and efficient diagnosis of disease. This model develops a hybrid Convolutional neural network (CNN) model to create efficient classifiers for both image classification and segmentation tasks. The images which are used as input are resized and normalized to recommend the exact size of the inputs with similar intensity which helps during feature extraction stage. Image segmentation mode segments labels for image using the K means clustering to perform as shown in figure relevant part of CNN (first associate the dots to their value). The images are then split into training and validation sets, and data augmentation is applied to maintain data diversity and avoid overfitting. The convolutional layers in our hybrid CNN architecture have conventional shapes, batch normalization and ReLU (for non-linear activation) are also a part of them, as well as a max-pooling layer to progressively extract more spatial features. In the final stages of feature extraction model performs the classification of images using fully connected, softmax and classification layers. This training is set on several epochs for the optimist of model weights with stochastic gradient descent with momentum. Once the model is trained, it is validated using a confusion matrix against the validation set and it performance of is found using evaluation metrics that provide precision recall, accuracy and F1 score. Compared to existing ones with combination of lighter based CNN for lung disease classification, our proposed hybrid CNN model is integrated with K-means clustering for better and augment advanced preprocessing, data augmentation, and multi-layer architecture for better feature extraction. Thus, such hybrid CNN represent to allowing real-time solutions like lung disease identification, medical imaging analysis and object recognition in clinical and resource-limited settings.