Unveiling Prognostic Pattern: A Hybrid Approach For Predictive Analytics in Medical Image Data

Currently, medical image data are being processed using deep learning technology to revolutionize disease-related research. Deep learning-based medical image analysis involves the application of various essential tasks, including medical image reformation, enhancement of image quality, segmentation, classification, and Computer-Aided Detection (CAD). These tasks aim to discern differences between healthy cells and tumour cells, facilitating more accurate diagnoses and effective treatment planning. This paper focuses on employing a hybrid approach for medical image analysis, integrating both clustering and classification techniques. The proposed methodology leverages k-means clustering, Visual Geometry Group (VGG), and Recurrent Neural Network (RNN) on distinct datasets related to cardiovascular diseases, breast cancer, and brain diseases. The hybrid approach aims to capitalize on the strengths of both clustering and classification methods, enhancing the overall accuracy and interpretability of the medical image analysis process. By utilizing diverse datasets, the research endeavours to contribute valuable insights into the identification and understanding of various diseases, ultimately advancing the field of medical image analysis for improved healthcare outcomes.