Optimized deep learning model for medical image diagnosis

Deep learning models, particularly convolutional neural networks (CNNs), have excelled in pattern recognition tasks like face recognition, pedestrian detection, and medical diagnosis. CNNs, known for their end-to-end feature extraction and classification, are widely used in computer vision. Pre-trained CNN models such as AlexNet, VGG-19, ResNet, and Inception can serve as feature extractors for various problems, though they often produce a large number of features. To address this challenge and reduce the number of features, this research employs an efficient two-layer feature selection algorithm. The first layer, a global search optimizer, guides the swarm in the search space to identify the optimal feature subset. The second layer refines and fine-tunes the solution obtained from the global search. The main contributions of this work encompass several aspects: (i) the introduction of a lightweight classification model that utilizes a subset of features, (ii) the use of an efficient optimizer to perform simultaneous feature and instance selection, and (iii) the direct implications for the radiology field, where it could serve as a supportive tool for diagnosis. To evaluate the proposed approach, it was tested on skin lesion diagnosis using 1113 abnormal and 1099 normal lesions. The pre-trained VGG-19 model provided a 1-D feature vector of size 4096, with a linear SVM used for classification. Results showed the optimizer reduced the feature vector by up to 86 % while achieving over 70 % accuracy. The proposed optimizer outperformed the Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).