Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is significant to detect the PD at the initial stages so that the person can live a peaceful life for a longer time period. The serious levels of PD are highly risky as the patients get progressive stiffness, which results in the inability of standing or walking. Earlier studies have focused on the detection of PD effectively using voice and speech exams and writing exams. In this aspect, this study presents an improved sailfish optimization algorithm with deep learning (ISFO-DL) model for PD diagnosis and classification. The presented ISFO-DL technique uses the ISFO algorithm and DL model to determine PD and thereby enhances the survival rate of the person. The presented ISFO is a metaheuristic algorithm, which is inspired by a group of hunting sailfish to determine the optimum solution to the problem. Primarily, the ISFO algorithm is applied to derive an optimal subset of features with a fitness function of maximum classification accuracy. At the same time, the rat swarm optimizer (RSO) with the bidirectional gated recurrent unit (BiGRU) is employed as a classifier to determine the existence of PD. The performance validation of the IFSO-DL model takes place using a benchmark Parkinson’s dataset, and the results are inspected under several dimensions. The experimental results highlighted the enhanced classification performance of the ISFO-DL technique, and therefore, the proposed model can be employed for the earlier identification of PD.

Recently, a drastic increase in medical imaging such as X-rays, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) offers essential anatomical and functional details related to different body parts for monitoring, treatment planning, detection, and classification. The use of Content-Based Medical Image Retrieval (CBMIR) technologies helps to handle massive amounts of medical images, and also encryption can be considered an effective solution to attain security in the CBMIR process. In this regard, this research develops an optimal deep transfer learning enabled secure CBMIR technique, called ODTL-SCBMIR model. The proposed ODTL-SCBMIR prototype aim is to provide image encryption techniques with retrieval procedures. To accomplish this, the presented ODTL-SCBMIR model initially employs multikey homomorphic encryption (MKHE) with an oppositional jellyfish search optimizer (OJSO) algorithm for security. Next, the image retrieval process includes a series of processes namely capsule network (CapsNet) based feature extraction, chaos game optimization (CGO) based hyperparameter optimizer, and Manhattan distance based similar measurement. The performance validation of the ODTL-SCBMIR prototype is experimented with by employing a set of medical images. The investigational results implied the enhanced performance of the ODTL-SCBMIR prototype over current approaches.

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological systems. Due to the advancements of medical imaging in healthcare decision making, significant attention has been paid by the computer vision and deep learning (DL) models. At the same time, the detection and classification of colorectal cancer (CC) become essential to reduce the severity of the disease at an earlier stage. The existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning (ML) to recognize the existence of diseases. In this aspect, this study focuses on the design of intelligent DL based CC detection and classification (IDL-CCDC) model for bioinformatics applications. The proposed IDL-CCDC technique aims to detect and classify different classes of CC. In addition, the IDL-CCDC technique involves fuzzy filtering technique for noise removal process. Moreover, water wave optimization (WWO) based EfficientNet model is employed for feature extraction process. Furthermore, chaotic glowworm swarm optimization (CGSO) based variational auto encoder (VAE) is applied for the classification of CC into benign or malignant. The design of WWO and CGSO algorithms helps to increase the overall classification accuracy. The performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.