B Ashwini, Kaur Manjit, Singh Dilbag, Roy Satyabrata, Amoon Mohammed
Department of ISE, NMAM Institute of Technology, Nitte (Deemed to be University), Nitte 574110, India.
School of Computer Science and Artificial Intelligence, SR University, Warangal 506371, India.
Diagnostics (Basel). 2023 Oct 17;13(20):3234. doi: 10.3390/diagnostics13203234.
Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor classification (BTC) models have shown significant progress, they encounter limitations like restricted depth, vanishing gradient issues, and difficulties in capturing intricate features. To address these challenges, this paper proposes an efficient skip connections-based residual network (ESRNet). leveraging the residual network (ResNet) with skip connections. ESRNet ensures smooth gradient flow during training, mitigating the vanishing gradient problem. Additionally, the ESRNet architecture includes multiple stages with increasing numbers of residual blocks for improved feature learning and pattern recognition. ESRNet utilizes residual blocks from the ResNet architecture, featuring skip connections that enable identity mapping. Through direct addition of the input tensor to the convolutional layer output within each block, skip connections preserve the gradient flow. This mechanism prevents vanishing gradients, ensuring effective information propagation across network layers during training. Furthermore, ESRNet integrates efficient downsampling techniques and stabilizing batch normalization layers, which collectively contribute to its robust and reliable performance. Extensive experimental results reveal that ESRNet significantly outperforms other approaches in terms of accuracy, sensitivity, specificity, F-score, and Kappa statistics, with median values of 99.62%, 99.68%, 99.89%, 99.47%, and 99.42%, respectively. Moreover, the achieved minimum performance metrics, including accuracy (99.34%), sensitivity (99.47%), specificity (99.79%), F-score (99.04%), and Kappa statistics (99.21%), underscore the exceptional effectiveness of ESRNet for BTC. Therefore, the proposed ESRNet showcases exceptional performance and efficiency in BTC, holding the potential to revolutionize clinical diagnosis and treatment planning.
脑肿瘤在医学诊断中构成了复杂且紧迫的挑战，由于其多样的特征和潜在的危及生命的后果，需要进行精确且及时的分类。虽然现有的基于深度学习（DL）的脑肿瘤分类（BTC）模型已取得显著进展，但它们存在诸如深度受限、梯度消失问题以及难以捕捉复杂特征等局限性。为应对这些挑战，本文提出了一种基于高效跳跃连接的残差网络（ESRNet），它利用带有跳跃连接的残差网络（ResNet）。ESRNet在训练期间确保梯度的平滑流动，减轻梯度消失问题。此外，ESRNet架构包括多个阶段，其中残差块数量不断增加，以改进特征学习和模式识别。ESRNet利用ResNet架构中的残差块，其具有能够实现恒等映射的跳跃连接。通过在每个块内将输入张量直接加到卷积层输出上，跳跃连接保留了梯度流。这种机制可防止梯度消失，确保在训练期间有效信息在网络层之间传播。此外，ESRNet集成了高效的下采样技术和稳定的批归一化层，这些共同促成了其强大且可靠的性能。大量实验结果表明，ESRNet在准确率、灵敏度、特异性、F分数和卡帕统计量方面显著优于其他方法，中位数分别为99.62%、99.68%、99.89%、99.47%和99.42%。此外，所实现的最低性能指标，包括准确率（99.34%）、灵敏度（99.47%）、特异性（99.79%）、F分数（99.04%）和卡帕统计量（99.21%），突出了ESRNet在BTC方面的卓越有效性。因此，所提出的ESRNet在BTC中展现出卓越的性能和效率，具有革新临床诊断和治疗规划的潜力。
