A bio‐inspired AlexNet‐DrpXLm architype for an effective brain stroke lesion detection and classification. (2nd June 2022)

• Record Type: Journal Article
• Authors: Melingi, Sunil Babu; Mojjada, Ramesh Kumar; Karunakar Reddy, Vanga; Pavan Kumar, Ch. V. M. S. N.; Ashok Kumar, K.
• Is Part Of: Concurrency and computation. Volume 34:Number 21(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-02
• Subjects: AlexNet -- brain stroke lesions -- CT images -- dropout extreme learning machine -- Himalayan Institute of Medical Sciences -- improved dolphin swarm optimization algorithm; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7100
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In this article, a bio‐inspired AlexNet‐DrpXLm architype is proposed for an effective brain stroke lesion detection and classification within a short period. Here, the input CT image datasets are collected from Himalayan Institute of Medical Sciences, then the images are preprocessed to take away the noises and also enhance the quality of the images. After that, the input images are trained and the features are extracted with the help of AlexNet model, and then classified as the brain images of normal and abnormal. The last three layers of the AlexNet model are replaced with the dropout extreme learning machine (DrpXLM) classifier to classify the images in efficient manner. The DrpXLM weight parameters are tuned by improved dolphin swarm optimization algorithm. The proposed method attains higher accuracy 99.54%, high precision 90.43%, high F‐score 89.40%, higher specificity 93.56%, higher sensitivity 93.56%, lower computational time 0.02 s, and the proposed method is compared to the existing methods, like random decision forest with gravitational search algorithm, hybrid native Bayes and sample weighted with random forest classification algorithm, and random forest with fractional‐order Darwinian particle swarm optimization.