Using deep learning to analyze medical images and predict health outcomes

Since pneumonia is a significant lung disease, prompt and accurate diagnosis is essential to make sure the treatment will help. A specialized CNN allows our system to automatically diagnose pneumonia from images of a patient’s chest X-ray. The method relies on the idea that the X-rays demonstrate the patient’s chest. Images of regular and affected lungs can be found in the X-rays included in the data we gathered on Kaggle. The data was preprocessed with image upscaling to 232×232×3 pixels, augmentation and using 80% for train and 20% for test sets. A convolutional neural network architecture was set up by starting with a dense layer and a classification layer, adding three convolutional layers with bigger filters, batch normalization, ReLU and max-pooling. Following the CNN, a fully connected layer was implemented. A total of fifteen epochs were used during training with the Adam optimizer. The model was evaluated according to its accuracy and the results presented in confusion matrices. The model’s behavior was better understood by viewing heatmaps and looking at the misclassification results. The model offers important help to healthcare teams by providing a reliable and automated method for spotting pneumonia where resources are limited.