A bio‐inspired AlexNet‐DrpXLm architype for an effective brain stroke lesion detection and classification

Abstract

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.

Review of AlexNet for Medical Image Classification

“…For instance, it can assist in identifying and delineating skin lesions, including moles, tumors [80], and other dermatological conditions in dermatology. Similarly, in the context of medical imaging, AlexNet can aid in identifying and delineating lesions or anomalies within organs [81][82][83], tissues [84], or anatomical structures [85], which is essential for treatment planning and disease monitoring.…”