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Journal of Intelligent Systems and Internet of Things

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Journal of Intelligent Systems and Internet of Things

Volume 16 , Issue 2 , PP: 308-324, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

AI and Machine Learning for Breast Cancer Diagnosis Using Histopathology and Clinical Decision Systems

Swati R. Nitnaware 1 * , Bindu Madhavi Tummala 2 , Naga Siva Jyothi Kompalli 3 , Lakshmi Ramani Burra 4 , Nelli Sreevidya 5 , Gunavardini V. 6

  • 1 Asst. Professor, Dept. of Electronics Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharastra, India - (swatitidke02@gmail.com)
  • 2 Assistant Professor, Department of CSE, Velagapudi Ramakrishna Siddhartha School of Engineering, Siddhartha Academy of Higher Education (Deemed to be University) Vijayawada, AP, India - (bindumadhavi@vrsiddhartha.ac.in)
  • 3 Associate Professor, Dept. of CSE, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India - (sivajyothi.p@sreenidhi.edu.in)
  • 4 Associate Professor, Dept. of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram AP, India - (ramanimythili@gmail.com)
  • 5 Assistant Professor , Department of IT, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India - (sreevidya.n@sreenidhi.edu.in)
  • 6 Assistant Professor, Department of CSE, Bannari Amman Institute of Technology (Autonomous), Sathyamangalam, Erode, TN, India - (gunavardini@bitsathy.ac.in)

    • Doi: https://doi.org/10.54216/JISIoT.160222

    Received: December 31, 2024 Revised: February 25, 2025 Accepted: March 31, 2025
    Abstract

    The diagnosis of breast cancer depends on histopathology for precise and trusted evaluation between malignant tumor cells and benign cells. The analysis demands significant time and creates additional room for human errors. A deep learning approach for computer-aided diagnosis (CAD) establishes techniques to enhance the classification performance in this study. The proposed methods utilize One-hot encoding with VGG-16 for feature extraction to achieve 98% accuracy with BreakHis data while DBN for feature learning reaches 98% accuracy on BreakHis and 96% on Kaggle. SSGAN addresses unannotated images effectively with up to 89% accuracy. Through its application, deep learning technology proves to enhance breast cancer identification while decreasing the workload on medical pathologists. One-hot encoding remains efficient for computations yet the DBN extraction method produces superior features. The SSGAN model increases labeling accuracy when it uses available labeled data and unlabeled data to lower annotation expenses. Deep learning technologies validate their ability to transform breast cancer histopathological diagnosis through precision-enhanced efficient examination methods especially with semi-supervised GAN systems.

    Keywords :

    Breast Cancer Diagnosis , Deep Learning , Computer-Aided Diagnosis (CAD) , CNNs , Deep Belief Network (DBN) , Semi-Supervised Generative Adversarial Network , Medical Image Analysis , Machine Learning in Healthcare , Tumor Classification

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    Cite This Article As :
    R., Swati. , Madhavi, Bindu. , Siva, Naga. , Ramani, Lakshmi. , Sreevidya, Nelli. , V., Gunavardini. AI and Machine Learning for Breast Cancer Diagnosis Using Histopathology and Clinical Decision Systems. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2025, pp. 308-324. DOI: https://doi.org/10.54216/JISIoT.160222
    R., S. Madhavi, B. Siva, N. Ramani, L. Sreevidya, N. V., G. (2025). AI and Machine Learning for Breast Cancer Diagnosis Using Histopathology and Clinical Decision Systems. Journal of Intelligent Systems and Internet of Things, (), 308-324. DOI: https://doi.org/10.54216/JISIoT.160222
    R., Swati. Madhavi, Bindu. Siva, Naga. Ramani, Lakshmi. Sreevidya, Nelli. V., Gunavardini. AI and Machine Learning for Breast Cancer Diagnosis Using Histopathology and Clinical Decision Systems. Journal of Intelligent Systems and Internet of Things , no. (2025): 308-324. DOI: https://doi.org/10.54216/JISIoT.160222
    R., S. , Madhavi, B. , Siva, N. , Ramani, L. , Sreevidya, N. , V., G. (2025) . AI and Machine Learning for Breast Cancer Diagnosis Using Histopathology and Clinical Decision Systems. Journal of Intelligent Systems and Internet of Things , () , 308-324 . DOI: https://doi.org/10.54216/JISIoT.160222
    R. S. , Madhavi B. , Siva N. , Ramani L. , Sreevidya N. , V. G. [2025]. AI and Machine Learning for Breast Cancer Diagnosis Using Histopathology and Clinical Decision Systems. Journal of Intelligent Systems and Internet of Things. (): 308-324. DOI: https://doi.org/10.54216/JISIoT.160222
    R., S. Madhavi, B. Siva, N. Ramani, L. Sreevidya, N. V., G. "AI and Machine Learning for Breast Cancer Diagnosis Using Histopathology and Clinical Decision Systems," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 308-324, 2025. DOI: https://doi.org/10.54216/JISIoT.160222

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