Article: Classification of malaria disease using optimal fire hawks-based capsule convolutional vision transformer network Journal: International Journal of Bio-Inspired Computation (IJBIC) 2026 Vol.28 No.1 pp.35 - 44 Abstract: Malaria is a widespread harmful disease caused by parasites transmitted through infected mosquitoes. The primary challenge addressed in this paper is the low performance, high error rates, and poor segmentation accuracy in malaria disease classification using existing machine learning (ML) and deep learning (DL) techniques. To overcome these limitations, this study proposes an advanced malaria classification model using the optimal fire Hawks-based capsule convolutional vision transformer network (OFH_C2ViTNet). The input image was segmented using feature concatenation-based U-Net (FC-UNet) based on red blood cells, white blood cells, and malaria parasites. Finally, the suggested optimal fire Hawks-based capsule convolutional vision transformer network (OFH_C2ViTNet) was used to categorise malaria into difficult, trophozoite, gametocyte, ring, and schizont kinds. From the input images, the proposed model provides an efficient result for identifying the disease at an early stage. Experimental evaluation demonstrates that the proposed model achieves superior performance, with an accuracy of 97.42% and a precision of 98.68%, outperforming existing state-of-the-art methods in malaria detection. Inderscience Publishers - linking academia, business and industry through research

Title: Classification of malaria disease using optimal fire hawks-based capsule convolutional vision transformer network

Authors: Pallavi Bhanudas Salunkhe; Pravin Sahebrao Patil

Addresses: Electronics and Telecommunication Department, SSVPS's Bapusaheb Shivajirao Deore College of Engineering, Dhule, Maharashtra, 424005, India ' Electronics and Telecommunication Department, SSVPS's Bapusaheb Shivajirao Deore College of Engineering, Dhule, Maharashtra, 424005, India

Abstract: Malaria is a widespread harmful disease caused by parasites transmitted through infected mosquitoes. The primary challenge addressed in this paper is the low performance, high error rates, and poor segmentation accuracy in malaria disease classification using existing machine learning (ML) and deep learning (DL) techniques. To overcome these limitations, this study proposes an advanced malaria classification model using the optimal fire Hawks-based capsule convolutional vision transformer network (OFH_C2ViTNet). The input image was segmented using feature concatenation-based U-Net (FC-UNet) based on red blood cells, white blood cells, and malaria parasites. Finally, the suggested optimal fire Hawks-based capsule convolutional vision transformer network (OFH_C2ViTNet) was used to categorise malaria into difficult, trophozoite, gametocyte, ring, and schizont kinds. From the input images, the proposed model provides an efficient result for identifying the disease at an early stage. Experimental evaluation demonstrates that the proposed model achieves superior performance, with an accuracy of 97.42% and a precision of 98.68%, outperforming existing state-of-the-art methods in malaria detection.

Keywords: median filtering; vision transformer network; feature concatenation; malaria classification; fire hawks optimiser.

DOI: 10.1504/IJBIC.2026.155395

International Journal of Bio-Inspired Computation, 2026 Vol.28 No.1, pp.35 - 44

Accepted: 05 Jul 2025
Published online: 31 Jul 2026 *

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Title: Classification of malaria disease using optimal fire hawks-based capsule convolutional vision transformer network

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