![]() This research was supported by Penelitian Terapan Unggulan Perguruan Tinggi (PTUPT) grant number No. The work supported by ERDF/ESF "Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)" (No. R3.1/HKP.05.00/2019 from the Ministry of the Research Technology Republic of Indonesia. T1 - Multipatch-GLCM for texture feature extraction on classification of the colon histopathology images using deep neural network with GPU acceleration The work supported by ERDF/ESF 2020 Toto Haryanto, Adib Pratama, Heru Suhartanto, Aniati Murni, Kusmardi Kusmardi and Jan Pidanic.", Note = "Funding Information: This research was supported by Penelitian Terapan Unggulan Perguruan Tinggi (PTUPT) grant number No. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.", According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72☐.48 for four cross-validations. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a meanshift filter as the data pre-processing of the images. This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch. The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data. The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. Not only for the pathologist but also from the view of a computer scientist. It is the main reason why research in this field becomes challenging. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.Ībstract = "Cancer is one of the leading causes of death in the world. ![]() ![]() Cancer is one of the leading causes of death in the world.
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