MRI Image Analysis with Deep Learning Methods in Brain Tumor Diagnosis

2022 ◽  
pp. 35-42
Author(s):  
Yasin Kırelli ◽  
Seher Arslankaya ◽  
Pelin Alcan
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Diagnosis ◽  
Learning Methods ◽  
Brain Tumor Diagnosis ◽  
Mri Image

Doctor's Dilemma: Evaluating an Explainable Subtractive Spatial Lightweight Convolutional Neural Network for Brain Tumor Diagnosis

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-26
Author(s):  
Ambeshwar Kumar ◽  
Ramachandran Manikandan ◽  
Utku Kose ◽  
Deepak Gupta ◽  
Suresh C. Satapathy
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Medical Image ◽  
Tumor Classification ◽  
Tumor Diagnosis ◽  
Trust Level ◽  
Brain Tumor Diagnosis

In Medicine Deep Learning has become an essential tool to achieve outstanding diagnosis on image data. However, one critical problem is that Deep Learning comes with complicated, black-box models so it is not possible to analyze their trust level directly. So, Explainable Artificial Intelligence (XAI) methods are used to build additional interfaces for explaining how the model has reached the outputs by moving from the input data. Of course, that's again another competitive problem to analyze if such methods are successful according to the human view. So, this paper comes with two important research efforts: (1) to build an explainable deep learning model targeting medical image analysis, and (2) to evaluate the trust level of this model via several evaluation works including human contribution. The target problem was selected as the brain tumor classification, which is a remarkable, competitive medical image-based problem for Deep Learning. In the study, MR-based pre-processed brain images were received by the Subtractive Spatial Lightweight Convolutional Neural Network (SSLW-CNN) model, which includes additional operators to reduce the complexity of classification. In order to ensure the explainable background, the model also included Class Activation Mapping (CAM). It is important to evaluate the trust level of a successful model. So, numerical success rates of the SSLW-CNN were evaluated based on the peak signal-to-noise ratio (PSNR), computational time, computational overhead, and brain tumor classification accuracy. The objective of the proposed SSLW-CNN model is to obtain faster and good tumor classification with lesser time. The results illustrate that the SSLW-CNN model provides better performance of PSNR which is enhanced by 8%, classification accuracy is improved by 33%, computation time is reduced by 19%, computation overhead is decreased by 23%, and classification time is minimized by 13%, as compared to state-of-the-art works. Because the model provided good numerical results, it was then evaluated in terms of XAI perspective by including doctor-model based evaluations such as feedback CAM visualizations, usability, expert surveys, comparisons of CAM with other XAI methods, and manual diagnosis comparison. The results show that the SSLW-CNN provides good performance on brain tumor diagnosis and ensures a trustworthy solution for the doctors.

Smear technique in the intra-operative brain tumor diagnosis: Its advantages and limitations

1999 ◽  
Vol 21 (1) ◽  
pp. 121-124 ◽  
Author(s):  
J. Slowiński ◽  
M. Harabin-Slowińska ◽  
R. Mrówka
Keyword(s):  
Brain Tumor ◽  
Tumor Diagnosis ◽  
Brain Tumor Diagnosis

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

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