A New Disease Detection System Using Transfer Learning Based on Firefly Optimization and DRB Classifier

Human beings are exposed to an infinite number of illnesses that can affect their health in multiple ways. The early detection of a disease can help doctors to manage it better by treating or at least controlling it. This research aims to help detect diseases from medical imaging. In this paper, we propose a new approach for disease detection from medical imaging based on VGG19 transfer learning with firefly optimization and Deep Rule Based (DRB) classifier. First, a set of features is extracted from the images using the pre-trained model VGG19. Next, we perform an optimization of the feature set by employing Firefly Algorithm (FA) to select the best subset of features that enhances the classifier performance and reduce the dataset complexity. Finally, DRB model is used to classify the samples. This method is tested on two image datasets, which concern two different diseases; Autism spectrum disorder (ASD) and Melanoma one of the most dangerous types of skin cancer. The first dataset is Autism_Image_Data, which comprises 2940 images of autistic and non-autistic children. The second dataset is a collection of 10605 images of benign and malignant skin lesions, called the “Melanoma Skin Cancer Dataset of 10000 Images”. To highlight the effectiveness of the system, it has been compared to several previous works. Experimental results have shown the best performance of the proposed approach compared to existing works. The proposed approach reached respectively 91.8 % and 83.33% of accuracy on the melanoma skin cancer dataset and the ASD dataset.