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A Hybrid Technique for Breast Cancer Detection with Efficient Imbalance Removal and Classification using ShuffleNetV2 Architecture
ID：132 View Protection：ATTENDEE Updated Time：2025-12-23 13:12:32 Hits：327 Online

Start Time：2025-12-30 16:00（Asia/Amman）

Duration：15min

Session：S2 Track 2: IoT and applications » S2-2Track 2: IoT and applications

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Abstract
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.
 
Keywords
Breast Cancer, INBreast Dataset, Imbalance, Hybrid CNN network, ShuffleNetV2.
Speaker
Debaleena Datta
Assistant Professor Dept. of Computer Science & Applications Techno Main Saltlake

Submission Author
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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Important Date

  • Conference Date

    Dec 29

    2025

    to

    Dec 31

    2025

  • Dec 20 2025

    Draft paper submission deadline

  • Dec 31 2025

    Contribution Submission Deadline

  • Dec 31 2025

    Registration deadline

Sponsored By
United Societies of Science
Organized By
Zarqa University
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