MR image enhancement for ICH classification

Intracranial hemorrhage (ICH) is a pathological disorder that necessitates quick diagnosis and decision making. Computed tomography (CT) is a precise and highly reliable diagnosis model to detect hemorrhages. Automated detection of ICH from CT scans with a computer-aided diagnosis (CAD) model is useful to detect and classify the different grades of ICH. Because of the latest advancement of deep learning (DL) models on image processing applications, several medical imaging techniques utilize it. This study develops a new densely connected convolutional network (DenseNet) with extreme learning machine (ELM)) for ICH diagnosis and classification, called DN-ELM. The presented DL-ELM model utilizes Tsallis entropy with a grasshopper optimization algorithm (GOA), named TEGOA, for image segmentation and DenseNet for feature extraction. Finally, an extreme learning machine (ELM) is exploited for image classification purposes. To examine the effective classification outcome of the proposed method, a wide range of experiments were performed, and the results are determined using several performance measures. The simulation results ensured that the DL-ELM model has reached a proficient diagnostic performance with the maximum accuracy of 96.34%.

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“…In the case of grasshopper action, social communication S i plays an important role, which is attained from Equation (9).…”

Section: Tegoa-based Segmentation Processmentioning

confidence: 99%

“…The published applications involve the prediction and diagnosis of skin cancer, pulmonary lumps, and cerebral micro-HM. In spite of the studies demonstrating the efficiency of ML for diagnostic medicine and radiology, medical implementation of DL technology remains unexplored [9,10].…”

Section: Introductionmentioning

confidence: 99%