Deep Learning for Cancer Diagnosis

1. Front Matter

   Pages i-xix

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2. Fusion of Deep Learning and Image Processing Techniques for Breast Cancer Diagnosis

   • V. Ajantha Devi, Anand Nayyar

   Pages 1-25

3. Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease

   • M. Sinan Basarslan, F. Kayaalp

   Pages 27-35

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

   • S. N. Kumar, A. Lenin Fred, Parasuraman Padmanabhan, Balazs Gulyas, H. Ajay Kumar, L. R. Jonisha Miriam

   Pages 37-66

5. Classification of Canine Fibroma and Fibrosarcoma Histopathological Images Using Convolutional Neural Networks

   • İsmail Kırbaş, Özlem Özmen

   Pages 67-77

6. Evaluation of Big Data Based CNN Models in Classification of Skin Lesions with Melanoma

   • Prasitthichai Naronglerdrit, Iosif Mporas

   Pages 79-98

7. Combined Radiology and Pathology Based Classification of Tumor Types

   • N. Ravitha Rajalakshmi, B. Sangeetha, R. Vidhyapriya, Nikhil Ramesh

   Pages 99-109

8. Improved Deep Learning Techniques for Better Cancer Diagnosis

   • K. R. Sekar, R. Parameshwaran, Rizwan Patan, R. Manikandan, Ambeshwar Kumar

   Pages 111-133

9. Using Deep Learning Techniques in Detecting Lung Cancer

   • Osamah Khaled Musleh Salman, Bekir Aksoy, Koray Özsoy

   Pages 135-146

10. Effective Use of Deep Learning and Image Processing for Cancer Diagnosis

    • J. Prassanna, Robbi Rahim, K. Bagyalakshmi, R. Manikandan, Rizwan Patan

    Pages 147-168

11. A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Various Representations of Cancerous Mass

    • Gokhan Altan

    Pages 169-187

12. Deep Learning for Brain Tumor Segmentation

    • Khushboo Munir, Fabrizio Frezza, Antonello Rizzi

    Pages 189-201

13. Convolutional Neural Network Approach for the Detection of Lung Cancers in Chest X-Ray Images

    • D. A. A. Deepal, T. G. I. Fernando

    Pages 203-226

14. Future of Deep Learning for Cancer Diagnosis

    • Pinar Koc, Cihan Yalcin

    Pages 227-238

15. Brain Tumor Segmentation Using 2D-UNET Convolutional Neural Network

    • Khushboo Munir, Fabrizio Frezza, Antonello Rizzi

    Pages 239-248

16. Use of Deep Learning Approaches in Cancer Diagnosis

    • M. Hanefi Calp

    Pages 249-267

17. Deep Learning for Magnetic Resonance Images of Gliomas

    • John J. Healy, Kathleen M. Curran, Amira Serifovic Trbalic

    Pages 269-300

This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed.


Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.

• Artificial intelligence
• Machine learning
• Medical diagnosis
• Autoencoder Networks
• Brain tumours
• Histopathological diagnosis
• Prostate cancer
• Early detection-diagnosis
• Medical data analyses
• Medical image processing
• Computational Neural Networks (CNN)
• Long short-term memory (LSTM)

• Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

  Utku Kose

• Faculty of Engineering, Al-Balqa Applied University, Balqa, Jordan

  Jafar Alzubi

Utku Kose received his Ph. D. degree in 2017 from Selcuk University, Turkey in the field of computer engineering. Currently, he is an Associate Professor in Suleyman Demirel University, Turkey. He has more than 100 publications to his credit. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, the chaos theory, distance education, e-learning, computer education, and computer science.

Jafar Alzubi received his PhD in Advanced Telecommunications Engineering  from Swansea University, UK, in 2012. He is currently an associate professor at the Computer Engineering Dept., Al-Balqa Applied University, Jordan. His research focuses on Elliptic curves cryptography and cryptosystems, classifications and detection of web scams, using Algebraic-Geometric theory in channel coding for wireless networks. He is currently working jointly with Wake Forest University, NC-USA as a visiting associate professor.​

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