AUTOMATIC DIAGNOSIS OF BREAST CANCER IN HISTOLOGY IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORKS

2018 ◽  
Author(s):  
Hung Le Minh ◽  
Manh Mai Van ◽  
Toan Tran Dinh ◽  
Tot Tran Dac ◽  
Tran Van Lang
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Automatic Diagnosis

Deep convolutional neural networks for classifying breast cancer using infrared thermography

2021 ◽  
pp. 1-12
Author(s):  
Juan Carlos Torres-Galván ◽  
Edgar Guevara ◽  
Eleazar Samuel Kolosovas-Machuca ◽  
Antonio Oceguera-Villanueva ◽  
Jorge L. Flores ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Infrared Thermography ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks

scholarly journals Deep convolutional neural networks for COVID‐19 automatic diagnosis

2021 ◽  
Author(s):  
Heba M. Emara ◽  
Mohamed R. Shoaib ◽  
Mohamed Elwekeil ◽  
Walid El‐Shafai ◽  
Taha E. Taha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Automatic Diagnosis

scholarly journals Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4

2021 ◽  
Author(s):  
Mohammed Abdulla Salim Al Husaini ◽  
Mohamed Hadi Habaebi ◽  
Teddy Surya Gunawan ◽  
Md Rafiqul Islam ◽  
Elfatih A. A. Elsheikh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Early Stage ◽  
Computational Cost ◽  
Optimization Methods ◽  
Optimization Method ◽  
Deep Convolutional Neural Networks ◽  
Breast Thermography

AbstractBreast cancer is one of the most significant causes of death for women around the world. Breast thermography supported by deep convolutional neural networks is expected to contribute significantly to early detection and facilitate treatment at an early stage. The goal of this study is to investigate the behavior of different recent deep learning methods for identifying breast disorders. To evaluate our proposal, we built classifiers based on deep convolutional neural networks modelling inception V3, inception V4, and a modified version of the latter called inception MV4. MV4 was introduced to maintain the computational cost across all layers by making the resultant number of features and the number of pixel positions equal. DMR database was used for these deep learning models in classifying thermal images of healthy and sick patients. A set of epochs 3–30 were used in conjunction with learning rates 1 × 10–3, 1 × 10–4 and 1 × 10–5, Minibatch 10 and different optimization methods. The training results showed that inception V4 and MV4 with color images, a learning rate of 1 × 10–4, and SGDM optimization method, reached very high accuracy, verified through several experimental repetitions. With grayscale images, inception V3 outperforms V4 and MV4 by a considerable accuracy margin, for any optimization methods. In fact, the inception V3 (grayscale) performance is almost comparable to inception V4 and MV4 (color) performance but only after 20–30 epochs. inception MV4 achieved 7% faster classification response time compared to V4. The use of MV4 model is found to contribute to saving energy consumed and fluidity in arithmetic operations for the graphic processor. The results also indicate that increasing the number of layers may not necessarily be useful in improving the performance.

scholarly journals Automatic diagnosis of disc bulge and disc herniation based on lumbar MR images using deep convolutional neural networks (Preprint)

2019 ◽  
Author(s):  
Qiong Pan ◽  
Xiyang Liu ◽  
Kai Zhang ◽  
Lin He ◽  
Zhou Dong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Magnetic Resonance ◽  
Convolutional Neural Networks ◽  
Disc Herniation ◽  
Lumbar Disc ◽  
Diagnostic Efficiency ◽  
Mr Images ◽  
Deep Convolutional Neural Networks ◽  
Automatic Diagnosis ◽  
Disc Bulge

BACKGROUND Lumbar abnormalities often lead to the lower back pain which has keep plaguing people’s lives. Magnetic resonance imaging (MRI) is one of the most efficient techniques to detect lumbar diseases and widely used in clinic. How to interpret massive amounts of magnetic resonance (MR) images quickly and accurately is an urgent problem. OBJECTIVE The aim of this study is to present an automatic system to diagnosis of disc bulge and herniation which is time-saving and effective so that can reduce radiologists’ workload. METHODS The diagnosis of disorders of lumbar vertebral is highly dependent on medical images, therefore, we choose two most common diseases disc bulge and herniation as the research objects. The study is mainly about classification of the axial lumbar disc MR images using deep convolutional neural networks. RESULTS This system comprises of four steps. First step, automatic localizes vertebral bodies (including L1, L2, L3, L4, L5, and S1, L: Lumbar, S: Sacral) in sagittal images using the Faster R-CNN and 4-fold cross-validations show 100% accuracy respectively. Second step, automatically determine the corresponding disc of each axial lumbar disc MR images with 100% accuracy. In the third step, the accuracy to automatic localizes intervertebral disc region of interest (ROI) in axial MR images is 100%. The three classification (disc normal, disc bulge and disc herniation) accuracies in the last step in five groups (L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1) are 92.7%, 84.4%, 92.1%, 90.4% and 84.2% respectively. CONCLUSIONS The automatic diagnosis system was successful built which can classify images of disc normal, disc bulge and disc herniation. This system provides an online test to interpret lumbar disc MR images which will be very helpful in improving the diagnostic efficiency and standardizing diagnosis reports, also, the system can be promoted to detect other lumbar abnormalities and cervical spondylosis.

scholarly journals Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

2019 ◽  
Author(s):  
Lourdes Duran-Lopez ◽  
Juan Dominguez-Morales ◽  
Isabel Amaya-Rodriguez ◽  
Francisco Luna-Perejon ◽  
Javier Civit-Masot ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Diagnosis ◽  
Diagnosis System
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