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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Krishnakumar B
School of Computing, SASTRA Deemed University, Tamil Nadu, India
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A Cross-Scale Spatial–Channel Attention Inception Network for Efficient Medical Image Segmentation Krishnakumar B; Nisha P; Sri Laxmi Kuna; Venu K; Evance Leethial R; Rama Krishna Kunchanapalli
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1550

Abstract

Medical image segmentation plays a crucial role in modern computerized diagnosis, as accurate delineation of anatomical structures directly impacts clinical decision-making and treatment planning. However, segmenting anatomically complex regions at a fine-grained level remains challenging, especially when computational efficiency is a key requirement. To address these challenges, the authors propose a novel, lightweight medical image segmentation framework, CSA-IncepLiteNet, designed to achieve high segmentation accuracy without imposing a significant computational burden. The CSA-IncepLiteNet architecture integrates two key innovations: cross-scale feature extraction and unified spatial channel attention learning. Central to this framework is the newly introduced Cross-Scale InceptionLite module, which efficiently captures multi-scale contextual information. This module is built using depth-wise separable convolutions and point-wise convolutions, enabling effective feature extraction while significantly reducing the number of trainable parameters. By learning features across multiple spatial scales, the network can better represent anatomically complex structures present in medical images. In addition, the authors propose a Cross-Scale Spatial Channel Attention (CSA) module that jointly models spatial saliency and channel-wise interdependencies within a unified attention-learning paradigm. This dual attention mechanism allows the network to focus on the most informative regions and feature channels simultaneously, leading to improved segmentation precision. The performance of CSA-IncepLiteNet was evaluated on the BUSI breast ultrasound dataset and multiple CT image modality-based datasets. Experimental results demonstrate that the proposed framework consistently outperforms existing state-of-the-art methods across all evaluated datasets. Notably, CSA-IncepLiteNet achieves an accuracy of 92.1% and a Dice coefficient of 82.94% on the BUSI dataset, while utilizing over 26 million fewer parameters than a conventional U-Net. These results highlight the model’s effectiveness, robustness, and suitability for resource-constrained medical imaging applications.
NeuroQFormer-Net: A Quaternion-Guided Hybrid Attention Transformer Fusion Network for Accurate Brain Tumor Segmentation Krishnakumar B; Thanga Parvathi B; Abbinayaa M; Kunchanapalli Rama Krishna; Suganthi Rajamanickam; Satheeswaran Venkatesan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1756

Abstract

Brain tumor segmentation is a critical task in medical image analysis, as accurate identification and delineation of tumor regions support timely diagnosis, treatment planning, therapeutic decision-making, and disease monitoring. Magnetic Resonance Imaging (MRI) provides multiple complementary modalities that offer valuable information about tumor structure and tissue characteristics. However, accurate segmentation remains challenging due to substantial variations in tumor size, shape, texture, and location, along with irregular and overlapping tumor boundaries, low-contrast regions, and considerable differences in the information captured by different MRI modalities. To address these challenges, this study proposes NeuroQFormer-Net (Quaternion-Guided Hybrid Attention Transformer Feature Fusion Network), a novel deep learning framework for multi-modal brain tumor segmentation. The proposed framework employs quaternion-based inter-channel correlation learning to effectively capture and integrate complementary information across multiple MRI modalities while preserving structural dependencies and enhancing cross-modal feature representations. In addition, a hybrid attention mechanism integrated with transformer-based learning is utilized to simultaneously capture fine-grained local structural characteristics and long-range contextual dependencies. To further improve segmentation accuracy, an adaptive co-learning feature fusion strategy is introduced to effectively integrate multi-scale representations obtained from different network levels. Furthermore, a boundary-aware refinement module enhances tumor boundary localization and reduces segmentation errors, particularly in low-contrast, irregular, and complex tumor regions. Experimental evaluation demonstrates the effectiveness of NeuroQFormer-Net, achieving an average Dice Similarity Coefficient (DSC) of 96.45%, Intersection over Union (IoU) of 93.26%, Precision of 96.17%, Recall of 96.08%, and Hausdorff Distance (HD) of 2.31 mm. These results indicate that the proposed framework provides accurate and robust multi-modal brain tumor segmentation
Co-Authors Abbinayaa M Evance Leethial R Kunchanapalli Rama Krishna Nisha P Rama Krishna Kunchanapalli Satheeswaran Venkatesan Sri Laxmi Kuna Suganthi Rajamanickam Thanga Parvathi B Venu K