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A Hybrid Technique for Breast Cancer Detection with Efficient Imbalance Removal and Classification using ShuffleNetV2 Architecture
编号：132 访问权限：仅限参会人 更新：2025-12-23 13:12:32 浏览：103次 拓展类型2

报告开始：2025年12月30日 16:00（Asia/Amman）

报告时间：15min

所在会场：[S2] Track 2: IoT and applications [S2-2] Track 2: IoT and applications

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摘要
For researchers to improve treatment efficacy and reduce mortality rates, early identification of breast cancer is crucial assisted by digitally enabled tools. In recent times, deep CNN-based deep transfer learning (TL) methods have emerged as the most facilitating technology. In this paper, we propose a computer aided methodology named deep hybrid AS-RF-ShuffNetV2 which addresses three major challenges of the base technology: data imbalance, extraction of feature set, and classification. Three steps make up the entire work: (1) Adaptive synthetic minority oversampling (ADASYN) is used to oversample malignant images (minority) in order to improve the feature space; (2) Random Forest (RF) splits are used to extract selective features from the balanced dataset; and (3) ShuffleNetV2, an efficient deep TL model, uses a channel split block and a decisive channel attention block to classify the final features into two target classes. The accuracy and AUC score achieved by our model are 92.68% and 0.86, using INBreast dataset. The strength and consistency of the suggested method in correctly identifying breast cancer classes are demonstrated by thorough testing versus existing contemporary approaches.
 
关键词
Breast Cancer, INBreast Dataset, Imbalance, Hybrid CNN network, ShuffleNetV2.
报告人
Debaleena Datta
Assistant Professor Dept. of Computer Science & Applications Techno Main Saltlake

稿件作者
Debaleena Datta Dept. of Computer Science & Applications Techno Main Saltlake
Uddalok Sen Dept. of Information Technology MCKV Institute of Enginnering Howrah, India
Hani Attar Zarqa University Jordon
Zakaria Che Muda Faculty of Engineering and Quantity Surveying INTI-IU University Nilai, Malaysia
Abey Jose School of Allied Health University of Limerick, Ireland
Muhammad Fazal Ijaz Torrens University Australia
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重要日期

  • 会议日期

    12月29日

    2025

    至

    12月31日

    2025

  • 12月30日 2025

    报告提交截止日期

  • 02月10日 2026

    初稿截稿日期

  • 02月10日 2026

    注册截止日期

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国际科学联合会
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扎尔卡大学
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