Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks.

IF 2.3 Q3 ENGINEERING, BIOMEDICAL Biomedical Engineering and Computational Biology Pub Date : 2024-10-28 eCollection Date: 2024-01-01 DOI:10.1177/11795972241278907
G Siva Shankar, Edeh Michael Onyema, Balasubramanian Prabhu Kavin, Venkataramaiah Gude, Bvv Siva Prasad
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Abstract

One of the leading causes of death for women worldwide is breast cancer. Early detection and prompt treatment can reduce the risk of breast cancer-related death. Cloud computing and machine learning are crucial for disease diagnosis today, but they are especially important for those who live in distant places with poor access to healthcare. While machine learning-based diagnosis tools act as primary readers and aid radiologists in correctly diagnosing diseases, cloud-based technology can also assist remote diagnostics and telemedicine services. The promise of techniques based on Artificial Neural Networks (ANN) for sickness diagnosis has attracted the attention of several re-searchers. The 4 methods for the proposed research include preprocessing, feature extraction, and classification. A Smart Window Vestige Deletion (SWVD) technique is initially suggested for preprocessing. It consists of Savitzky-Golay (S-G) smoothing, updated 2-stage filtering, and adaptive time window division. This technique separates each channel into multiple time periods by adaptively pre-analyzing its specificity. On each window, an altered 2-stage filtering process is then used to retrieve some tumor information. After applying S-G smoothing and integrating the broken time sequences, the process is complete. In order to deliver effective feature extraction, the Deep Residual based Multiclass for architecture (DRMFA) is used. In histological photos, identify characteristics at the cellular and tissue levels in both tiny and large size patches. Finally, a fresh customized strategy that combines a better crow forage-ELM. Deep learning and the Extreme Learning Machine (ELM) are concepts that have been developed (ACF-ELM). When it comes to diagnosing ailments, the cloud-based ELM performs similarly to certain cutting-edge technology. The cloud-based ELM approach beats alternative solutions, according to the DDSM and INbreast dataset results. Significant experimental results show that the accuracy for data inputs is 0.9845, the precision is 0.96, the recall is 0.94, and the F1 score is 0.95.

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利用虚拟化和基于深度前馈网络的极限学习算法诊断乳腺癌。
乳腺癌是导致全球妇女死亡的主要原因之一。早期发现和及时治疗可以降低与乳腺癌相关的死亡风险。云计算和机器学习对于当今的疾病诊断至关重要,但对于那些生活在遥远地方、医疗条件差的人来说尤为重要。基于机器学习的诊断工具可以作为初级阅读器,帮助放射科医生正确诊断疾病,而基于云计算的技术也可以帮助远程诊断和远程医疗服务。基于人工神经网络(ANN)的疾病诊断技术的前景吸引了一些研究人员的关注。拟议研究的 4 种方法包括预处理、特征提取和分类。预处理最初采用的是智能窗口删除(SWVD)技术。它包括萨维茨基-戈莱(S-G)平滑、更新的两级滤波和自适应时间窗口划分。该技术通过自适应预分析每个信道的特异性,将其分为多个时间段。然后,在每个窗口上使用改变的 2 级滤波过程来检索一些肿瘤信息。在应用 S-G 平滑处理并整合破碎的时间序列后,整个过程就完成了。为了提供有效的特征提取,使用了基于深度残差的多类架构(DRMFA)。在组织学照片中,识别微小和大尺寸斑块中细胞和组织层面的特征。最后,一种全新的定制策略结合了更好的乌鸦饲养--ELM。深度学习和极限学习机(ELM)是已经开发出来的概念(ACF-ELM)。在诊断疾病方面,基于云的 ELM 的表现与某些尖端技术类似。根据 DDSM 和 INbreast 数据集的结果,基于云的 ELM 方法击败了其他解决方案。重要的实验结果显示,数据输入的准确度为0.9845,精确度为0.96,召回率为0.94,F1得分为0.95。
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