Medical Image Analysis Based on Deep Learning Approach for Early Diagnosis of Diseases
S Balasubramaniam, A. Prasanth, K. Satheesh Kumar, V. Kavitha
Abstract
Medical image analysis has become a key technology for medical improvement. It involves using and studying images of the human body, usually from an X-ray, a CT scan, or an MRI, imaging technology has come a long way to detect diseases, schedule surgeries, and conduct research. Medical image analysis allows extensive, non-invasive anatomy investigation. With the rapid growth of artificial intelligence (AI), deep learning (DL) based techniques for analyzing medical images are becoming more widespread. Deep neural network, powerful strategy for computers to learn by themselves that can automatically analyze medical images for disease detection and assessment. Numerous AI-based solutions automate medical picture processing by detecting, segmenting, classifying, and analyzing lesions, tumors, clots, and obstructions. DL models can recognize patterns and relationships in medical images using large amounts of data. DL allows higher abstraction and better dataset prediction. Therefore, DL has had a major impact and gained popularity recently. In this chapter, common medical imaging modalities and deep learning-based technologies for medical image processing tasks like classification, detection, segmentation, severity grading, and prediction are explained.