AMF-Net: An adaptive multisequence fusing neural network for multi-modality brain tumor diagnosis

2022 ◽  
Vol 72 ◽  
pp. 103359
Author(s):  
Zheng Huang ◽  
Yiwen Zhao ◽  
Yunhui Liu ◽  
Guoli Song
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Tumor Diagnosis ◽  
Brain Tumor Diagnosis

scholarly journals FWNNet: Presentation of a New Classifier of Brain Tumor Diagnosis Based on Fuzzy Logic and the Wavelet-Based Neural Network Using Machine-Learning Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mohsen Ahmadi ◽  
Fatemeh Dashti Ahangar ◽  
Nikoo Astaraki ◽  
Mohammad Abbasi ◽  
Behzad Babaei
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Tumor Diagnosis ◽  
Training Process ◽  
Feature Extraction Method ◽  
Gradient Descent Algorithm ◽  
Brain Tumor Diagnosis ◽  
The Brain

In this paper, we present a novel classifier based on fuzzy logic and wavelet transformation in the form of a neural network. This classifier includes a layer to predict the numerical feature corresponded to labels or classes. The presented classifier is implemented in brain tumor diagnosis. For feature extraction, a fractal model with four Gaussian functions is used. The classification is performed on 2000 MRI images. Regarding the results, the accuracy of the DT, KNN, LDA, NB, MLP, and SVM is 93.5%, 87.6%, 61.5%, 57.5%, 68.5%, and 43.6%, respectively. Based on the results, the presented FWNNet illustrates the highest accuracy of 100% with the fractal feature extraction method and brain tumor diagnosis based on MRI images. Based on the results, the best classifier for diagnosis of the brain tumor is FWNNet architecture. However, the second and third high-performance classifiers are the DT and KNN, respectively. Moreover, the presented FWNNet method is implemented for the segmentation of brain tumors. In this paper, we present a novel supervised segmentation method based on the FWNNet layer. In the training process, input images with a sweeping filter should be reshaped to vectors that correspond to reshaped ground truth images. In the training process, we performed a PSO algorithm to optimize the gradient descent algorithm. For this purpose, 80 MRI images are used to segment the brain tumor. Based on the results of the ROC curve, it can be estimated that the presented layer can segment the brain tumor with a high true-positive rate.

scholarly journals A deep neural network based classifier for brain tumor diagnosis

2019 ◽  
Vol 82 ◽  
pp. 105528 ◽  
Author(s):  
Ambeshwar Kumar ◽  
Manikandan Ramachandran ◽  
Amir H. Gandomi ◽  
Rizwan Patan ◽  
Szymon Lukasik ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Deep Neural Network ◽  
Tumor Diagnosis ◽  
Brain Tumor Diagnosis

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 Cancer Imaging and Brain Tumor Diagnosis

2017 ◽  
Vol 09 (01) ◽  
Author(s):  
Hamidreza Shirzadfar ◽  
Samin Riahi ◽  
Mahsa Sadat Ghaziasgar
Keyword(s):  
Brain Tumor ◽  
Cancer Imaging ◽  
Tumor Diagnosis ◽  
Brain Tumor Diagnosis
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