基于深度学习的加权k近邻元启发式乳腺癌严重程度分类算法

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2023-06-01 DOI:10.1016/j.irbm.2022.100749
S.R. Sannasi Chakravarthy , N. Bharanidharan , H. Rajaguru
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引用次数: 11

摘要

癌症在女性中最广泛和最具侵入性的类型是癌症。在全球范围内,这种类型的癌症导致的女性死亡率更高,仅次于癌症。这使得研究人员更加专注于开发有效的计算机辅助检测(CAD)方法来对这种致命的癌症类型进行分类。为了提高生存率和早期诊断,癌症的分类需要一种乐观的研究方法。因此,提出了一种将深度学习原理与元启发式和分类算法相结合的改进方法,用于癌症的严重程度分类。因此,为了加强最近的发现,提出了一种改进的CAD方法来解决医疗保健问题。材料和方法这项工作旨在为对数字乳房X光图像中存在的严重程度进行分类提供研究。为了评估工作,使用了公开的MIAS、INbreast和WDBC数据库。拟议的工作采用迁移学习来提取特征。该工作的新颖性在于,使用粒子群优化(PSO)、龙飞优化算法(DFOA)和乌鸦搜索优化算法(CSOA)作为一种转换技术,即将非线性输入特征转换为最小线性可分离特征向量,提高了加权k近邻(wKNN)算法的分类性能。结果将所提出的工作获得的结果与高斯朴素贝叶斯算法和线性支持向量机算法进行比较,其中所提出工作(CSOA wKNN)的分类精度最高,MIAS数据集的分类精度为84.35%,INbreast数据集为83.19%,WDBC数据集为97.36%。结论所提出的计算机辅助诊断(CAD)工具对癌症的严重程度分类具有较强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer

Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer

Objective

The most widespread and intrusive cancer type among women is breast cancer. Globally, this type of cancer causes more mortality among women, next to lung cancer. This made the researchers to focus more on developing effective Computer-Aided Detection (CAD) methodologies for the classification of such deadly cancer types. In order to improve the rate of survival and earlier diagnosis, an optimistic research methodology is required in the classification of breast cancer. Consequently, an improved methodology that integrates the principle of deep learning with metaheuristic and classification algorithms is proposed for the severity classification of breast cancer. Hence to enhance the recent findings, an improved CAD methodology is proposed for redressing the healthcare problem.

Material and Methods

The work intends to cast a light-of-research towards classifying the severities present in digital mammogram images. For evaluating the work, the publicly available MIAS, INbreast, and WDBC databases are utilized. The proposed work employs transfer learning for extricating the features. The novelty of the work lies in improving the classification performance of the weighted k-nearest neighbor (wKNN) algorithm using particle swarm optimization (PSO), dragon-fly optimization algorithm (DFOA), and crow-search optimization algorithm (CSOA) as a transformation technique i.e., transforming non-linear input features into minimal linear separable feature vectors.

Results

The results obtained for the proposed work are compared then with the Gaussian Naïve Bayes and linear Support Vector Machine algorithms, where the highest accuracy for classification is attained for the proposed work (CSOA-wKNN) with 84.35% for MIAS, 83.19% for INbreast, and 97.36% for WDBC datasets respectively.

Conclusion

The obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the severity classification of breast cancer.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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