Brain Tumor Classification based on MR Images using GAN as a Pre-Trained Model

Dinesh Reddy Yerukalareddy, Evgeniy Pavlovskiy

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

In the medical industry, misdiagnosis of disease is acknowledged as the most common and harmful medical errors as it can cost a human life. Radiologists require a lot of time to manually annotate and segment the images. Over the several years, deep learning has been playing a vital role in the field of computer vision. One of its key uses in the medical industry is to minimize misdiagnosis and the amount of time taken to annotate and segment the images. In this paper, a new deep learning approach for brain tumor classification on MRI Images is introduced. A deep neural network is pretrained as a discriminator in a generative adversarial network (GAN) on MR Images by using multi-scale gradient GAN (MSGGAN) with auxiliary classification to extract the features and to classify the images. In the discriminator, one of the fully connected blocks acts as an auxiliary classifier and the other fully connected block acts as an adversarial. The fully connected layers of the auxiliary block are fine-tuned to classify the type of tumor. The proposed approach is tested on two publicly available MRI datasets as a whole, consists of four types of brain tumors (glioma, meningioma, pituitary, and no tumor). Our proposed method achieved 98.57% accuracy which is better as compared to state of art methods. Also, our method appears to be a useful technique when the availability of medical images is limited.
Язык оригиналаанглийский
Название основной публикацииProceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021
ИздательIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Страницы380-384
Число страниц5
ISBN (электронное издание)978-1-6654-3149-1
ISBN (печатное издание)978-1-6654-3150-7
DOI
СостояниеОпубликовано - 26 мая 2021

Серия публикаций

НазваниеProceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021

Предметные области OECD FOS+WOS

  • 1.02 КОМПЬЮТЕРНЫЕ И ИНФОРМАЦИОННЫЕ НАУКИ

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