Brain tumor classification for combining the advantages of multilayer dense net‐based feature extraction and hyper‐parameters tuned attentive dual residual generative adversarial network classifier using wild horse optimization

• Published in: NMR in biomedicine Vol. 37; no. 12; pp. e5246 - n/a
• Main Authors: Anantharajan, Shenbagarajan, Gunasekaran, Shenbagalakshmi, Sujana, J. Angela Jennifa
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.12.2024
• Subjects: Algorithms; Animals; Brain; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain tumors; BraTS MRI image dataset; Classification; Datasets; Error analysis; Feature extraction; Generative adversarial networks; glioma image segmentation and registration; Homogeneity; Horses; Humans; Image quality; multilayer dense net feature extraction; Multilayers; Neural Networks, Computer; Optimization; Optimization algorithms; Parameter sensitivity; Tumors; Wavelet Analysis; Wavelet transforms; wild horse optimization algorithm; glioma image segmentation and registration; BraTS MRI image dataset; wild horse optimization algorithm; multilayer dense net feature extraction
• ISSN: 0952-3480; 1099-1492; 1099-1492
• DOI: 10.1002/nbm.5246

In this manuscript, attentive dual residual generative adversarial network optimized using wild horse optimization algorithm for brain tumor detection (ADRGAN‐WHOA‐BTD) is proposed. Here, the input imageries are gathered using BraTS, RemBRANDT, and Figshare datasets. Initially, the images are preprocessed to increase the quality of images and eliminate the unwanted noises. The preprocessing is performed with dual‐tree complex wavelet transform (DTCWT). The image features like geodesic data and texture features like contrasts, energy, correlations, homogeneity, and entropy are extracted using multilayer dense net methods. Then, the extracted images are given to attentive dual residual generative adversarial network (ADRGAN) classifier for classifying the brain imageries. The ADRGAN weight parameters are tuned based on wild horse optimization algorithm (WHOA). The proposed method is executed in MATLAB. For the BraTS dataset, the ADRGAN‐WHOA‐BTD method achieved accuracy, sensitivity, specificity, F‐measure, precision, and error rates of 99.85%, 99.82%, 98.92%, 99.76%, 99.45%, and 0.15%, respectively. Then, the proposed technique demonstrated a runtime of 13 s, significantly outperforming existing methods. In this manuscript, attentive dual residual generative adversarial network optimized using wild horse optimization algorithm for brain tumor detection (ADRGAN‐WHOA‐BTD) is proposed. Here, the input imageries are gathered using BraTS, RemBRANDT and Figshare datasets. Initially, the images are preprocessed to enhance quality and eliminate unwanted noises. The preprocessing is performed with dual‐tree complex wavelet transform (DTCWT). The image features like geodesic data and texture features like contrasts, energy, correlations, homogeneity, and entropy are extracted using multilayer dense net methods.