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[Online]A Hybrid Technique for Breast Cancer Detection with Efficient Imbalance Removal and Classification using ShuffleNetV2 Architecture
A Hybrid Technique for Breast Cancer Detection with Efficient Imbalance Removal and Classification using ShuffleNetV2 Architecture
ID：132
Submission ID：121 View Protection：ATTENDEE
Updated Time：2025-12-23 13:12:32 Hits：285
Online
Start Time：2025-12-30 16:00 （Asia/Amman）
Duration：15min
Session：[S2] Track 2: IoT and applications » [S2-2] Track 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 SaltlakeSubmission Author
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