Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and
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ORIGINAL ARTICLE
Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks Mesut Toğaçar 1
&
Burhan Ergen 1
&
Zafer Cömert 2
Received: 16 April 2020 / Accepted: 11 November 2020 # International Federation for Medical and Biological Engineering 2020
Abstract Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance images was used. The BrainMRNet model includes three processing steps: attention modules, the hypercolumn technique, and residual blocks. To demonstrate the accuracy of the proposed model, three types of tumor data leading to brain cancer were examined in this study: glioma, meningioma, and pituitary. In addition, a segmentation method was proposed, which additionally determines in which lobe area of the brain the two classes of tumors that cause brain cancer are more concentrated. The classification accuracy rates were performed in the study; it was 98.18% in glioma tumor, 96.73% in meningioma tumor, and 98.18% in pituitary tumor. At the end of the experiment, using the subset of glioma and meningioma tumor images, it was determined which at brain lobe the tumor region was seen, and 100% success was achieved in the analysis of this determination. In this study, a hybrid deep learning model is presented to determine the detection of the brain tumor. In addition, open-source software was proposed, which statistically found in which lobe region of the human brain the brain tumor occurred. The methods applied and tested in the experiments have shown promising results with a high level of accuracy, precision, and specificity. These results demonstrate the availability of the proposed approach in clinical settings to support the medical decision regarding brain tumor detection. Keywords Brain tumor . Attention module . Magnetic resonance image . Hypercolumn technique . Image processing . Medical segmentation
1 Introduction Brain tumor results from abnormal growth of brain cells. If tumors contain cancerous cells, it can lead to brain cancer.
* Mesut Toğaçar [email protected] Burhan Ergen [email protected] Zafer Cömert [email protected] 1
Department of Computer Technology, Technical Sciences Vocational School, Fırat University, Elazig, Turkey
2
Department of Software Engineering, Faculty of Engineering, Samsun University, Samsun, Turkey
Tumors that develop in the brain are usually seen in the pituitary gland, pineal gland, membranes covering the brain, cranial nerves, and tissues close to it [1, 2]. Brain cancer can be composed of malignant cells in the brain, or it can metastasize to the brain from other organs of the body [3]. Among the cancer types that have metastases
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