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Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future

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Deep Learning for Cancer Diagnosis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 908))

Abstract

Health care sector is entirely different from other industrial sector owing to the value of human life and people gives the highest priority. Medical image processing is a research domain where advance computer-aided algorithms are used for disease prognosis and treatment planning. Machine learning comprises of neural networks and fuzzy logic algorithms that have immense applications in the automation of a process. The deep learning algorithm is a machine learning technique that does not relies on feature extraction unlike classical neural network algorithms. Computer-aided automatic processing is in high demand in the medical field due to the improved accuracy and precision. The coupling of machine learning algorithms with high-performance computing gives promising results in medical image analysis like fusion, segmentation, registration and classification. This chapter proposes the applications of deep learning algorithms in cancer diagnosis specifically in the CT/MR brain and abdomen images, mammogram images, histopathological images and also in the detection of diabetic retinopathy. The overview of deep learning algorithms in cancer diagnosis, challenges and future scope is also highlighted in this work. The pros and cons of various types of deep learning neural network architectures are also stated in this work. The intelligent machines in future will be using the deep learning algorithms for the disease diagnosis, treatment planning and surgery.

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Acknowledgements

The authors would like to acknowledge the support provided by Nanyang Technologıcal Unıversıty under NTU Ref: RCA-17/334 for providing the medical images and supporting us in the preparation of the manuscript. Parasuraman Padmanabhan and Balazs Gulyas also acknowledge the support from Lee Kong Chian School of Medicine and Data Science and AI Research (DSAIR) centre of NTU (Project Number ADH-11/2017-DSAIR) and the support from the Cognitive NeuroImaging Centre (CONIC) at NTU.

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Authors and Affiliations

  1. Amal Jyothi College of Engineering, Kanjirappally, Kerala, India

    S. N. Kumar

  2. Mar Ephraem College of Engineering and Technology, Elavuvilai, India

    A. Lenin Fred, H. Ajay Kumar & L. R. Jonisha Miriam

  3. Nanyang Technological University, Singapore, Singapore

    Parasuraman Padmanabhan & Balazs Gulyas

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  1. S. N. Kumar
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  2. A. Lenin Fred
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  3. Parasuraman Padmanabhan
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  4. Balazs Gulyas
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  5. H. Ajay Kumar
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Correspondence to S. N. Kumar .

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  1. Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

    Utku Kose

  2. Faculty of Engineering, Al-Balqa Applied University, Balqa, Jordan

    Jafar Alzubi

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Kumar, S.N., Lenin Fred, A., Padmanabhan, P., Gulyas, B., Ajay Kumar, H., Jonisha Miriam, L.R. (2021). Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future. In: Kose, U., Alzubi, J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-15-6321-8_3

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