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Breast Cancer Classification Using Transfer Learning

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Evolving Technologies for Computing, Communication and Smart World

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 694))

Abstract

Cancer is one of the most lethal forms of the disease. And in females, breast cancer is the most common cancer which could even lead to death if not properly diagnosed. Over the years, a lot of advancement can be seen in the field of medical technology but when it comes to detecting breast cancer biopsy is the only way. Pathologists detect cancer by using histological images under the microscope. Inspecting cancer visually is a critical task; it requires a lot of attention, skill and is time-consuming. Therefore, there is a need for a faster and efficient system for detecting breast cancer. Advancements in the field of machine learning and image processing lead to multiple types of research for creating an efficient partially or fully computer monitored diagnosis system. In this paper, we have used histological images to detect and classify invasive ductal carcinoma. Our approach involves convolutional neural networks which are a very advanced and efficient technique when dealing with images in machine learning. We compared various famous deep learning models, and we used these pre-trained CNN architectures with fine-tuning to provide an efficient solution. We also used image augmentation to further improve the efficiency of the solution. In this study, we used VGG, ResNet, DenseNet, MobileNet, EfficientNet. The best result we got was using fine-tuned VGG19 and with proper image augmentation. We achieved a sensitivity of 93.05% and a precision of 94.46 with the mentioned architecture. We improved the F-Score of the latest researches by 10.2%. We have achieved an accuracy of 86.97% using a pre-trained DenseNet model which is greater than the latest researches that achieved 85.41% [30] accuracy.

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Authors and Affiliations

  1. JSS Academy of Technical Education, Noida, India

    Animesh Seemendra, Rahul Singh & Sukhendra Singh

Authors
  1. Animesh Seemendra
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  2. Rahul Singh
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  3. Sukhendra Singh
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Corresponding author

Correspondence to Animesh Seemendra .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Pradeep Kumar Singh

  2. CDAC Noida, Noida, Uttar Pradesh, India

    Arti Noor

  3. Department of Electrical Engineering, Indian Institute of Technology, Patna, Bihar, India

    Maheshkumar H. Kolekar

  4. Department of Computer Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

  5. Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA

    Raj K. Bhatnagar

  6. JSSATEN Noida, Noida, Uttar Pradesh, India

    Shaweta Khanna

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Seemendra, A., Singh, R., Singh, S. (2021). Breast Cancer Classification Using Transfer Learning. In: Singh, P.K., Noor, A., Kolekar, M.H., Tanwar, S., Bhatnagar, R.K., Khanna, S. (eds) Evolving Technologies for Computing, Communication and Smart World. Lecture Notes in Electrical Engineering, vol 694. Springer, Singapore. https://doi.org/10.1007/978-981-15-7804-5_32

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  • DOI: https://doi.org/10.1007/978-981-15-7804-5_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7803-8

  • Online ISBN: 978-981-15-7804-5

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