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Convolutional Neural Network-Based Cancer Detection Using Histopathologic Images

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Innovations in Machine and Deep Learning

Part of the book series: Studies in Big Data ((SBD,volume 134))

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Abstract

Cancer is one of the major health issues currently. Amongst different types of cancer in women, breast cancer stands in the second-highest place. Compared to other kinds of cancer, breast cancer mortality is high. The histopathological analysis is still considered the typical method of identifying cancer, even with the swift developments in medical disciplines. Analysis of histopathological images is time-consuming due to its complexity, and additionally, the results pertain to pathologist subjectivity. Therefore, developing robust and precise cancer detection using histopathological image analysis methods is essential. In this chapter, we discuss the problem of Cancer Detection using a deep learning-based approach. We provide details of the convolutional neural network (CNN), a neural network used for image datasets. Next, we describe various hyper-optimization techniques to optimize the training of the CNN network. Furthermore, we implement Cancer Detection by proposing a learning-based framework on the open-source Histopathologic Cancer Detection dataset. This dataset is available on Kaggle. The proposed framework uses CNN to detect cancer using advanced deep learning frameworks like TensorFlow. This approach can be extended to classify histopathologic images in other biomedical areas.

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Author information

Authors and Affiliations

  1. Applied Research Center, Florida International University, Miami, FL, USA

    Jayesh Soni

  2. Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USA

    Nagarajan Prabakar

  3. Electrical and Computer Engineering, Florida International University, Miami, FL, USA

    Himanshu Upadhyay

Authors
  1. Jayesh Soni
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  2. Nagarajan Prabakar
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  3. Himanshu Upadhyay
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Corresponding author

Correspondence to Jayesh Soni .

Editor information

Editors and Affiliations

  1. División Multidisciplinaria de Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    Gilberto Rivera

  2. Universidad Tecnológica de La Habana, La Habana, Cuba

    Alejandro Rosete

  3. School of Engineering, University of Cadiz, Cadiz, Spain

    Bernabé Dorronsoro

  4. Instituto Tecnológico de Ciudad Madero, Tecnológico Nacional de México, Tamaulipas, Mexico

    Nelson Rangel-Valdez

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Soni, J., Prabakar, N., Upadhyay, H. (2023). Convolutional Neural Network-Based Cancer Detection Using Histopathologic Images. In: Rivera, G., Rosete, A., Dorronsoro, B., Rangel-Valdez, N. (eds) Innovations in Machine and Deep Learning. Studies in Big Data, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-031-40688-1_13

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