Titlebook: Deep Learning for Cancer Diagnosis; Utku Kose,Jafar Alzubi Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive lice

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Using Deep Learning Techniques in Detecting Lung Cancer,er types. For this reason, the early diagnosis of lung cancer is very important for human health. Computed Tomography (CT) images are frequently utilized in the detection of lung cancer. In this book section, academic studies on the diagnosis of lung cancer are examined.

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Deep Learning for Brain Tumor Segmentation,learning is nowadays a very promising approach to develop effective solution for clinical diagnosis. This chapter provides at first some basic concepts and techniques behind brain tumor segmentation. Then the imaging techniques used for brain tumor visualization are described. Later on, the dataset and segmentation methods are discussed.

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Book 2021cer 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 vit

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Brain Tumor Segmentation Using 2D-UNET Convolutional Neural Network,tial variability. In this study, we propose a 2D-UNET model based on convolutional neural networks (CNN). The model is trained, validated and tested on BRATS 2019 dataset. The average dice coefficient achieved is 0.9694.

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Fusion of Deep Learning and Image Processing Techniques for Breast Cancer Diagnosis, It builds an efficient algorithm based on multiple processing (hidden) layers of neurons. Manual assessment of Cancer using Medical Image (CT images) requires expensive human labors and can easily cause the misdiagnose of any type of cancer. The Researcher focus on automatically diagnosing cancer b

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Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease,es of both medical diagnosing and medical treatment systems are increasing day by day. Cancer is the most common causes of death in today’s world and is generally diagnosed at the last stages. Cancer has many types such as breast cancer, skin cancer, leukemia and etc. Diagnosis of cancer at early st

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

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,Classification of Canine Fibroma and?Fibrosarcoma Histopathological Images Using Convolutional Neurgh resolution real histopathological microscope images has been developed. In order to determine the network performance, the well-known network models (VGG16, ResNET50, MobileNet-V2 and Inception-V3) were subjected to training and testing according to the same hardware and training criteria. While

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Combined Radiology and Pathology Based Classification of Tumor Types, the core of tumor diagnosis. Pathology images provide clinical information about the tissues whereas the radiology images can be used for locating the lesions. This work aims at proposing a classification model which categorizes the tumor as oligodendroglioma (benign tumors) (or) astrocytoma (Malig