scholarly journals Classification of Different Cancer Types by Deep Convolutional Neural Networks

2018 ◽  
pp. 56-59 ◽  
Author(s):  
H. SELCUK NOGAY
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Cancer Types

scholarly journals Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks

2018 ◽  
Vol 38 (3) ◽  
Author(s):  
Miao Wu ◽  
Chuanbo Yan ◽  
Huiqiang Liu ◽  
Qian Liu
Keyword(s):  
Neural Networks ◽  
Ovarian Cancer ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
Training Data ◽  
Serous Carcinoma ◽  
Deep Convolutional Neural Networks ◽  
Ovarian Cancers ◽  
Cancer Types

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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