An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Zia Ur Rehman, Muhammad and Ahmad, Jawad and Jaha, Emad Sami and Ali, Abdullah Marish and Alzain, Mohammed and Saeed, Faisal (2023) An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model. Computer Systems Science and Engineering, 46 (1). ISSN 0267-6192

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Breast cancer is one of the leading cancers among women. It has the second-highest mortality rate in women after lung cancer. Timely detection, especially in the early stages, can help increase survival rates. However, manual diagnosis of breast cancer is a tedious and time-consuming process, and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience. However, computer-aided medical diagnosis has recently shown promising results, leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages. The research presented in this paper is focused on the multi-class classification of breast cancer. The deep transfer learning approach has been utilized to train the deep learning models, and a pre-processing technique has been used to improve the quality of the ultrasound dataset. The proposed technique utilizes two deep learning models, Mobile- NetV2 and DenseNet201, for the composition of the deep ensemble model. Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results. Subsequently, entropy-based feature selection is used. Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%, while the sensitivity and F1 score were 96.87% and 96.76%, respectively. The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

Item Type: Article
Identification Number:
15 November 2022Accepted
20 January 2023Published Online
Uncontrolled Keywords: Breast cancer, image enhancement, ensemble model, transfer learning, feature selection
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 29 Nov 2022 11:15
Last Modified: 22 Mar 2023 14:44

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