標題: Titlebook: Deep Learning for Cancer Diagnosis; Utku Kose,Jafar Alzubi Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive lice [打印本頁] 作者: 密度 時間: 2025-3-21 16:08
書目名稱Deep Learning for Cancer Diagnosis影響因子(影響力)
書目名稱Deep Learning for Cancer Diagnosis影響因子(影響力)學科排名
書目名稱Deep Learning for Cancer Diagnosis網(wǎng)絡(luò)公開度
書目名稱Deep Learning for Cancer Diagnosis網(wǎng)絡(luò)公開度學科排名
書目名稱Deep Learning for Cancer Diagnosis被引頻次
書目名稱Deep Learning for Cancer Diagnosis被引頻次學科排名
書目名稱Deep Learning for Cancer Diagnosis年度引用
書目名稱Deep Learning for Cancer Diagnosis年度引用學科排名
書目名稱Deep Learning for Cancer Diagnosis讀者反饋
書目名稱Deep Learning for Cancer Diagnosis讀者反饋學科排名
作者: 滔滔不絕地說 時間: 2025-3-21 21:13 作者: Meager 時間: 2025-3-22 00:52 作者: colloquial 時間: 2025-3-22 06:07
Ilia Bider,Paul Johannesson,Erik Perjonsgh 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 作者: Palpable 時間: 2025-3-22 11:16 作者: Memorial 時間: 2025-3-22 14:31
Designing Organizational Systems 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作者: Memorial 時間: 2025-3-22 20:20
J?rn Flohr Nielsen,Henrik Bendixen S?rensenld, advancements in software, hardware and precise and fine-tune images acquired from sensors. With the advancement in the field of medical and applications of Artificial Intelligence scaling to the height of improvement, modern state-of-the-art applications of Deep Learning for better cancer diagno作者: LEVY 時間: 2025-3-22 23:48 作者: A簡潔的 時間: 2025-3-23 03:34 作者: 有常識 時間: 2025-3-23 08:46 作者: GENUS 時間: 2025-3-23 10:05 作者: Isthmus 時間: 2025-3-23 13:58
Designing Physical Interaction Platformsy difficult to identify lung nodules using raw chest X-ray images and analysis of such medical images has become a very complicated and tedious task. This study mainly concerned on convolutional neural network approach to identify whether a suspicious area is a nodule or a non-nodule. The JSRT digit作者: allergen 時間: 2025-3-23 18:42
Research Approach: Taxonomy Development These tumors are mostly by magnetic resonance imaging (MRI) from which the segmentation becomes a big problem because of the large structural and spatial variability. In this study, we propose a 2D-UNET model based on convolutional neural networks (CNN). The model is trained, validated and tested o作者: orthopedist 時間: 2025-3-24 01:34 作者: arboretum 時間: 2025-3-24 05:21 作者: 咒語 時間: 2025-3-24 08:50
Utku Kose,Jafar AlzubiHighlights recent advanced applications of Deep Learning for diagnosing cancer.Discusses relevant solutions for medical diagnosis using techniques such as CNN, LSTM, and Autoencoder Networks.Offers a 作者: nitric-oxide 時間: 2025-3-24 11:49
Studies in Computational Intelligencehttp://image.papertrans.cn/d/image/264603.jpg作者: FLACK 時間: 2025-3-24 14:54 作者: 向前變橢圓 時間: 2025-3-24 19:33 作者: Benign 時間: 2025-3-25 02:25 作者: 本土 時間: 2025-3-25 05:21
1860-949X niques such as CNN, LSTM, and Autoencoder Networks.Offers a 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 count作者: Scintillations 時間: 2025-3-25 11:10 作者: 極肥胖 時間: 2025-3-25 11:42 作者: glacial 時間: 2025-3-25 17:23
Designing Organizational Systemsobtained using pre-trained Inception v3 model. The resulting vectors are then used as input to the linear SVM (Support Vector Machine) classification model. The SVM model provided an accuracy of 75% on the blind folded test dataset provided in the competition.作者: 遺傳學 時間: 2025-3-25 23:37
,Classification of Canine Fibroma and?Fibrosarcoma Histopathological Images Using Convolutional Neurmuch higher performance value and training time is shorter than others. Thanks to low prediction error rate achieved with FibroNET network using real data, it seems possible to develop an artificial intelligence-based reliable decision support system that will facilitate surgeons’ decision making in practice.作者: 獨裁政府 時間: 2025-3-26 01:55 作者: 向下 時間: 2025-3-26 07:07
Designing Organizational Systemsst performance was achieved by re-training a modified version of ResNet-50 convolutional neural network with accuracy equal to 93.89%. Analysis on skin lesion pathology type was also performed with classification accuracy for melanoma and basal cell carcinoma being equal to 79.13 and 82.88%, respectively.作者: 異端邪說2 時間: 2025-3-26 09:41 作者: 欲望小妹 時間: 2025-3-26 13:23
Opening up the Innovation Processlearning 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.作者: acquisition 時間: 2025-3-26 16:52
Evaluation of Big Data Based CNN Models in Classification of Skin Lesions with Melanoma,st performance was achieved by re-training a modified version of ResNet-50 convolutional neural network with accuracy equal to 93.89%. Analysis on skin lesion pathology type was also performed with classification accuracy for melanoma and basal cell carcinoma being equal to 79.13 and 82.88%, respectively.作者: 假設(shè) 時間: 2025-3-26 22:40
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.作者: 谷類 時間: 2025-3-27 04:31
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.作者: 碎石頭 時間: 2025-3-27 09:04
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作者: Synchronism 時間: 2025-3-27 12:13
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.作者: 直言不諱 時間: 2025-3-27 13:53
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作者: 作嘔 時間: 2025-3-27 21:27
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作者: alleviate 時間: 2025-3-28 00:07
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 作者: atopic-rhinitis 時間: 2025-3-28 02:39
,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 作者: 激怒 時間: 2025-3-28 08:11 作者: 知道 時間: 2025-3-28 13:37
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作者: Spina-Bifida 時間: 2025-3-28 14:40 作者: conception 時間: 2025-3-28 21:57
Using Deep Learning Techniques in Detecting Lung Cancer,s, cancer diseases, in particular, are one of the important types of diseases that cause fatal outcomes. The World Health Organization stated that approximately 9.6 million people died from cancer worldwide in 2018. According to the World Health Organization, among these cancer types, approximately 作者: 愛國者 時間: 2025-3-29 02:56 作者: Compassionate 時間: 2025-3-29 05:28 作者: 航海太平洋 時間: 2025-3-29 08:28 作者: COWER 時間: 2025-3-29 13:09
Convolutional Neural Network Approach for the Detection of Lung Cancers in Chest X-Ray Images,y difficult to identify lung nodules using raw chest X-ray images and analysis of such medical images has become a very complicated and tedious task. This study mainly concerned on convolutional neural network approach to identify whether a suspicious area is a nodule or a non-nodule. The JSRT digit作者: CREST 時間: 2025-3-29 18:38 作者: thwart 時間: 2025-3-29 20:04 作者: landmark 時間: 2025-3-30 02:54 作者: 沉積物 時間: 2025-3-30 06:19
Fusion of Deep Learning and Image Processing Techniques for Breast Cancer Diagnosis,ased assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images, pp. 929–932, 2017 [.]). Identification of most cancer might facilitate in sparing a massive wide variety of lives over the globe community and deep neural networks may be correctly used for intelligent 作者: 擁護者 時間: 2025-3-30 10:20
Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease,rning data repository. Feature selection by information gain and reliefF were applied on datasets before classification in order to increase the efficiency of classification processes. Support Vector Machines (SVM), Random Forest (RF), Recurrent Neural Network (RNN) and Convolutional Neural Network 作者: dearth 時間: 2025-3-30 14:39 作者: Geyser 時間: 2025-3-30 20:03
Improved Deep Learning Techniques for Better Cancer Diagnosis,gorithm has resulted in great success resulting in robust image characteristics, involving higher dimensions. Analysis of bi-cubic interpolation preprocessing technique paves way for robust obtaining of a region of interest. For an inflexible object with a higher amount of dissimilarity, a comprehen作者: Demonstrate 時間: 2025-3-30 21:39
Effective Use of Deep Learning and Image Processing for Cancer Diagnosis,ls that are superior compared to manually obtained features of pixels are said to be learned. Supervised Discriminating Deep Learning directly provides discriminating potentiality for cancer diagnosis purposes. Finally, hybrid deep learning for labeled and unlabeled data is specifically used for can作者: 絕種 時間: 2025-3-31 02:42
A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Variousess. Mammography images are the most effective and simplest way of the diagnosis of breast cancer. Whereas early diagnosis of breast cancer is a hard process due to characteristics of mammography, the computer-assisted diagnosis systems have ability to perform a detailed analysis for a complete asse
