Toto Haryanto

This is my personal weblog…

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Posted by totoharyanto on May 27th, 2021

Cancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. 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. This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a mean-shift filter as the data pre-processing of the images. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72±0.48 for four cross-validations. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.

Further link : https://thescipub.com/abstract/10.3844/jcssp.2020.280.294


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Conditional sliding windows: An approach for handling data limitation in colorectal histopathology image classification

Posted by totoharyanto on May 23rd, 2021

Large amounts of data are required for the training process with a convolutional neural network (CNN) because small datasets with low variation will cause over-fitting, and the model cannot predict new data with high accuracy. Additionally, the non-availability of histopathological medical data presents an issue because without ethical permission, such data cannot be obtained easily. Therefore, this study proposes a conditional sliding window algorithm to obtain sub-sample data on images of histopathology.

Two sets of original data were used, one from the Warwick dataset with dimensions of 775 × 522 pixels and the other from the Department of Pathology and Anatomy, Faculty of Medicine Universitas Indonesia. The algorithm used was inspired by the conventional sliding window method, but implemented with added conditions, such as sliding the window algorithm from the left on (x,y) pixel coordinates, thereby moving from left to right, then up to down until the entire image was covered. Consequently, the new image was produced with two dimensions: 200 × 200 and 300 × 300 pixels. However, to avoid loss of information, the 25 and 50 pixels overlap were used. In this study, CNN 7-5-7 was designed and proposed to perform the process.

The conditional sliding window algorithm can produce various sub-samples depending on the image and window size. Furthermore, the images produced were used to develop a CNN and were proven to accurately predict benign and malignant tissues compared to the model from the original dataset. Moreover, the sensitivity values of the Warwick public dataset and the one generated in this study are above 0.80, which shows that the proposed CNN architecture is more stable compared to the existing methods such as AlexNet and DenseNet121.

This study succeeded in solving the limitations of colorectal histopathological training data by developing a conditional sliding window algorithm. This algorithm can be applied to generate other histopathological data. Moreover, our proposed CNN 7-5-7 is the fastest architecture for training, comparable to state-of-the-art methodologies. Furthermore, the dataset was used to develop the model for colorectal cancer identification and integrated on the web-based application for further implementation.

Further link : Conditional sliding windows: An approach for handling data limitation in colorectal histopathology image classification – ScienceDirect


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Ied Mubarok 1442 H

Posted by totoharyanto on May 13th, 2021

Selamat Hari Raya Iedul Fitri 1442 H

 تَقَبَّلَ اللَّهُ مِنَّا وَمِنْكُمْ, وَأَحَالَهُ اللَّهُ

Semoga Allah menerima segala ibadah puasa kita.
Mohon maaf lahir dan bathin.

Toto Haryanto & Keluarga


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Melakukan Recovery File jika terkena Ransomware .igdm

Posted by totoharyanto on December 20th, 2020

Beberapa waktu yang lalu, laptop saya mengalami hal yang tidak biasanya.
Entah mengapa semua file yang ada berekstensi .igdm. Setelah melakukan searching, ternyata ini adalah salah satu varian dari Ransomware.
Video ini memberikan penjelassan bagaimana caranya untuk menghilangan virus tersebut dan melakukan backup data jika sudah terlanjur terinfeksi.
Semoga bermanfaat ….

 


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