Quantum Fruit Fly algorithm and ResNet50-VGG16 for medical diagnosis - Nijaguna, G.S.; Babu, J. Ananda; Parameshachari, B.D.; de Prado, Rocío Pérez; Frnda, Jaroslav (Applied soft computing, 2023)
Medical data are present in large amount and this is difficult to process for the diagnosis and Healthcare organization requires effective technique to handle big data. Existing techniques in medical diagnosis have limitations of imbalance data and overfitting problem. This research applies Quantum Fruit Fly Algorithm (QFFA) technique for feature selection to improve the effectiveness of classification in medical diagnosis. The Min–Max Normalization technique is applied to normalize the images to reduce the difference in pixel values and enhance the images. The ResNet50 and VGG16 deep learning models were applied for the feature extraction. The QFFA technique applies Archimedes spiral to increases the exploitation of the model to select unique features for classification. The Archimedes spiral provides spiral search in the top solutions of the Fruit Fly algorithm that helps to overcome local optima trap and increases exploitation. The QFFA technique selected features were applied to SVM model for the effective classification of medical diseases. The QFFA unique feature selection helps to overcome imbalance and overfitting problem in classification. The QFFA model has achieved better results in terms of various performance metrics such as sensitivity (99.26 %), and accuracy (99.04%) than existing Deep 1D-CNN and GA-Decision tree models. •The major idea of this paper is to resolve the imbalance and overfitting issues.•Then Min–Max Normalization is applied to lessen the disparity in pixel values.•For Feature Extraction, the deep learning models ResNet50 and VGG16 were used.•The QFFA technique applies Archimedes spiral to increases the model exploitation.•The selected features from QFFA were applied to SVM for effective classification.