Deep Learning for Cancer Diagnosis

✍ Scribed by Utku Kose, Jafar Alzubi

• Publisher: Springer Singapore;Springer
• Year: 2021
• Tongue: English
• Leaves: 311
• Series: Studies in Computational Intelligence 908
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xix
Fusion of Deep Learning and Image Processing Techniques for Breast Cancer Diagnosis (V. Ajantha Devi, Anand Nayyar)....Pages 1-25
Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease (M. Sinan Basarslan, F. Kayaalp)....Pages 27-35
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
Classification of Canine Fibroma and Fibrosarcoma Histopathological Images Using Convolutional Neural Networks (İsmail Kırbaş, Özlem Özmen)....Pages 67-77
Evaluation of Big Data Based CNN Models in Classification of Skin Lesions with Melanoma (Prasitthichai Naronglerdrit, Iosif Mporas)....Pages 79-98
Combined Radiology and Pathology Based Classification of Tumor Types (N. Ravitha Rajalakshmi, B. Sangeetha, R. Vidhyapriya, Nikhil Ramesh)....Pages 99-109
Improved Deep Learning Techniques for Better Cancer Diagnosis (K. R. Sekar, R. Parameshwaran, Rizwan Patan, R. Manikandan, Ambeshwar Kumar)....Pages 111-133
Using Deep Learning Techniques in Detecting Lung Cancer (Osamah Khaled Musleh Salman, Bekir Aksoy, Koray Özsoy)....Pages 135-146
Effective Use of Deep Learning and Image Processing for Cancer Diagnosis (J. Prassanna, Robbi Rahim, K. Bagyalakshmi, R. Manikandan, Rizwan Patan)....Pages 147-168
A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Various Representations of Cancerous Mass (Gokhan Altan)....Pages 169-187
Deep Learning for Brain Tumor Segmentation (Khushboo Munir, Fabrizio Frezza, Antonello Rizzi)....Pages 189-201
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
Future of Deep Learning for Cancer Diagnosis (Pinar Koc, Cihan Yalcin)....Pages 227-238
Brain Tumor Segmentation Using 2D-UNET Convolutional Neural Network (Khushboo Munir, Fabrizio Frezza, Antonello Rizzi)....Pages 239-248
Use of Deep Learning Approaches in Cancer Diagnosis (M. Hanefi Calp)....Pages 249-267
Deep Learning for Magnetic Resonance Images of Gliomas (John J. Healy, Kathleen M. Curran, Amira Serifovic Trbalic)....Pages 269-300

✦ Subjects

Engineering; Computational Intelligence; Cancer Research; Health Informatics; Computer Imaging, Vision, Pattern Recognition and Graphics; Signal, Image and Speech Processing