Hybrid Bio-Inspired Optimization Framework for Deep Learning-Based Medical Image Analysis

Abstract

With the development of huge medical image data sets and complex diseases, medical image analysis has become an indispensable factor in the computer-aided diagnosis, which plays an important role in the automatic diagnosis, detection and classification of diseases. Medical image analysis has been an essential part of the computer-aided diagnosis, which has an important role in automatic detection and classification of complex diseases from large-scale medical image data sets. However, the conventional deep learning models have redundant features, high computation complexity and suboptimal hyperparameter selection which restricts the diagnostic performance of the model. The study introduces a Hybrid Bio-Inspired Optimization Framework for deep learning-based medical image analysis, which combines cutting-edge pre-processing methods, smart feature selection and adaptive optimization techniques to form a holistic framework. Medical images are first enhanced, normalized, augmented and ROI extracted to enhance the quality of the image and minimize noise. A hybrid bio-inspired feature selection mechanism is used to select the most discriminative features, with a minimum of redundancy and computational cost. Then, a novel optimized deep neural network is trained with the help of new optimization algorithms which are inspired by living organisms such as Adaptive Medical Image Preprocessing Algorithm (AMIPA), Hybrid Bio-Inspired Feature Selection Algorithm (HBIFSA), Hybrid Bio-Inspired Hyperparameter Optimization Algorithm (HBIHOA), and Optimized Deep Learning Training Algorithm (ODLTA). The integrated optimization strategy is experimentally validated to greatly improve the diagnostic performance with reduced computational requirements, which enables it to be adapted to intelligent clinical decision-support systems and next-generation AI-assisted health care applications.