Enhancing Medical Image Classification Through PSO-Optimized Dual Deterministic Approach and Robust Transfer Learning

Effective transfer learning, within medical image classification, is probably one of the most critical areas of research due to the associated complexities with the nature of medical images. These involve variations in the acquisition techniques of images, resolutions, modalities, and patient demographics. This study aims to develop a robust transfer learning model by combining Mobile-Net with Particle Swarm Optimization, called MOB-CFPSO that classifies efficiently variety of datasets across heterogenic dataset modalities, including colored, black, and white datasets. A high-performing machine learning model needs enough data to enable robust feature extraction that can identify patterns in each class, allowing the model to differentiate between different classes accurately. The current statistical algorithm is less effective in the case of colored and black image classification and they also produce errors in the classification of findings of obvious disease in heterogenic image datasets e.g. color, Black, and white. There is a need to design and implement a robust image classification model based on heterogenic datasets that will reduce the error rate and optimize classification accuracy by using the Transfer Learning technique. Model robustness in deep learning refers to the capacity to sustain performance and deliver accurate predictions, even in the face of uncertainties, disturbances, or adversarial conditions. Two separate experiments are conducted on a pre-trained Mobile-Net model along with Modified PSO with constriction factor for model optimization, accuracy enhancement, and reduction of error rate for disease classification on two distinct datasets. DR Dataset comprised 10 distinct, whereas the MRI brain tumor included 8 various classes. The results showed that the proposed Model MOB-CFPSO exceeds the state-of-the-art algorithm in datasets, achieving high accuracy, robustness, precision, recall, and F1-score, with acceptable and favorable validation loss. A comprehensive MRI study showed that the MOB-CFPSO achieved 100% and 99.86% of training and validation accuracy respectively, with 0.0071% and 0.0071% of validation and training loss. However, Precision, Recall, and F1 scores were 100%, 100%, and 100%, respectively. DR Dataset achieved 95.09% and 97.66% of training and validation accuracy respectively whereas validation loss was 0.1902%, and training loss was 0.2469%. Precision, Recall, and F1 score were 98.3%, 92.4%, and 95.3% respectively.