Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features

Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods; (iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework’s performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100× and 400× magnifications. To establish OralNet’s validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5%. These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.

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Enhancing Parkinson's Disease Diagnosis Through Mayfly-Optimized CNN BiGRU Classification

Dhanaskaran,

Chinnathambi,

Ravi

et al. 2024

Advances in Medical Technologies and Clinical Practice

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Neuron degeneration in the human brain causes Parkinson's disease (PD). The technique employed for detecting early PD is based on a parametric analysis of acoustic sounds. Machine learning methods such as SVM, KNN, decision tree, and random forest are used in a significant manner. When compared to machine learning, deep-learning (DL) algorithms always produce greater results. DL algorithms are utilized to assess the patient's voice record. In this work, the authors introduce the MAYFLY optimization algorithm, a nature-inspired metaheuristic optimization technique. The approach utilizes convolutional neural networks (CNNs) coupled with bi-directional gated recurrent units (BiGRUs) to leverage the temporal and spatial features of biomedical data. Experimental evaluations reveal that the proposed approach produces better accuracy of 98.31% and better precision rate (98.78%). These results demonstrate the potential of the MAYFLY-based CNN-BiGRU model in differentiating PD patients from healthy individuals.

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Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features

Cited by 3 publications

References 35 publications

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Automatic Detection of Waterbodies from Satellite Images Using DeepLabV3+

OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection

Enhancing Parkinson's Disease Diagnosis Through Mayfly-Optimized CNN BiGRU Classification

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