Brain tumor based mri image enhancement using entropy and clahe based intuitionistic fuzzy method with deep learning
TL;DR: In this paper , the authors presented an approach for the segmentation and classification of brain tumors using Entropy and CLAHE (Contrast Limited Adaptive Histogram Equalization) based Intuitionistic Fuzzy Method with Deep Learning.
Abstract: The inner area of the human brain is where abnormal brain cells gather when they become a mass. These are known as brain tumors, and based on the location and size of the tumor, they can produce a wide range of symptoms. Accurate segmentation and classification of brain tumors are critical for effective diagnosis and treatment planning. In this paper, we present a novel approach for the segmentation and classification of brain tumors using Entropy and CLAHE Based Intuitionistic Fuzzy Method with Deep Learning. Entropy and CLAHE (Contrast Limited Adaptive Histogram Equalization) based Intuitionistic Fuzzy Method with Deep Learning is a technique that combines several image processing and machine learning algorithms to enhance the quality of images. By applying entropy-based techniques to an image, we can identify and highlight the most significant features or patterns in the image. Our study provides a thorough evaluation of the proposed technique and its performance compared to other methods, showing its effectiveness and potential for use in real-world applications. Our method separates the tumor regions from the healthy tissue and provides accurate results in comparison with traditional methods. The results of this study demonstrate the potential of this approach to improve the diagnosis and treatment of brain tumors and provide a foundation for future research in this field. The proposed technique holds significant promise for improving the prognosis and quality of life for patients with brain tumors.
TL;DR: The study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions and implemented a convolutional neural network model framework to train the model for this challenge.
Abstract: A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest-performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions. Faster CNN used the VGG 16 architecture as a primary network to generate convolutional feature maps, then classified these to yield tumor region suggestions. The prediction accuracy was used to assess performance. Our suggested methodology was evaluated on a dataset for brain tumor diagnosis using MR images comprising 253 MRI brain images, with 155 showing tumors. Our approach could identify brain tumors in MR images. In the testing data, the algorithm outperformed the current conventional approaches for detecting brain tumors (Precision = 96%, 98.15%, 98.41% and F1-score = 91.78%, 92.6% and 91.29% respectively) and achieved an excellent accuracy of CNN 96%, VGG 16 98.5% and Ensemble Model 98.14%. The study also presents future recommendations regarding the proposed research work.
••01 Dec 2018
TL;DR: This paper proposes the state of art tumor detection techniques using the Watershed Dynamic Angle Projection - Convolution Neural Network (WDAPP-CNN), which accurately segments the tumor region and the dynamic angle projection pattern extracts the textured features of the brain.
Abstract: Brain tumor detection is a tedious task in the field of medical imaging. Detection or identification of brain tumor involves segmentation of brain image, extraction of brain features and classification of abnormality in the MRI brain image. This paper proposes the state of art tumor detection techniques using the Watershed Dynamic Angle Projection - Convolution Neural Network (WDAPP-CNN). The watershed algorithm accurately segments the tumor region. The dynamic angle projection pattern extracts the textured features of the brain and the convolutional neural network classifies the tumor and non-tumor regions of the MRI brain image. The abnormality of the brain image is detected and testing is achieved through the BRATS dataset in an efficient way.
TL;DR: Experimental results show that the proposed automated brain tumor segmentation method using rough-fuzzy C-means (RFCM) has achieved better performance based on statistical volume metrics than previous state-of-the-art algorithms with respect to ground truth (manual segmentation).
Abstract: Automated brain tumor segmentation of MR image is a very challenging task in a medical point of view. As the nature of the tumor, it can appear anywhere in the brain region with any size, shape, and contrast, that makes the segmentation process more difficult. In order to handle such issues, present work proposes an automated brain tumor segmentation method using rough-fuzzy C-means (RFCM) and shape based topological properties. In rough-fuzzy C-means, overlapping partition is efficiently handled by fuzzy membership and uncertainty in the datasets is resolved by lower and upper bound of the rough set. Fuzzy boundary and crisp lower approximation in RFCM play an effective contribution in brain tumor segmentation on MR images. Initial centroids selection is a major issue in C-means algorithms. Present work has introduced a method for initial centroids selection by which the execution time of RFCM is reduced as compared to random initial centroids. A patch based K-means method is also implemented for skull stripping as a preprocessing step. The proposed method was tested on MRI standard benchmark datasets. Experimental results show that the proposed method has achieved better performance based on statistical volume metrics than previous state-of-the-art algorithms with respect to ground truth (manual segmentation). It is also experimentally noticed that RFCM method achieves most promising results with higher accuracy than HCM (hard C-means) and FCM (fuzzy C-means).
TL;DR: A novel multiclass brain tumor classification method based on deep feature fusion that performed better than the existing systems and achieved accuracy of 99.7%; hence, it can be used in clinical setup to classify brain tumors from MRIs.
Abstract: Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have proposed automated methods in recent years to detect brain tumors early. These approaches, however, encounter difficulties due to their low accuracy and large false-positive values. An efficient tumor identification and classification approach is required to extract robust features and perform accurate disease classification. This paper proposes a novel multiclass brain tumor classification method based on deep feature fusion. The MR images are preprocessed using min-max normalization, and then extensive data augmentation is applied to MR images to overcome the lack of data problem. The deep CNN features obtained from transfer learned architectures such as AlexNet, GoogLeNet, and ResNet18 are fused to build a single feature vector and then loaded into Support Vector Machine (SVM) and K-nearest neighbor (KNN) to predict the final output. The novel feature vector contains more information than the independent vectors, boosting the proposed method's classification performance. The proposed framework is trained and evaluated on 15,320 Magnetic Resonance Images (MRIs). The study shows that the fused feature vector performs better than the individual vectors. Moreover, the proposed technique performed better than the existing systems and achieved accuracy of 99.7%; hence, it can be used in clinical setup to classify brain tumors from MRIs.
••07 Mar 2019
TL;DR: Experimental results prove that the proposed contrast limited adaptive histogram equalization and entropy-based intuitionistic fuzzy method gives better visual quality and provides high values of subjective and quantitative metrics compared to several states of art algorithms.
Abstract: Mortality rate because of breast cancer diminishes to a large extent if the categorization of breast lesions as malignant or benign is done properly. But this process is quite complicated owing to erroneous detection of noise pixels as false positives. It can be reduced by proper enhancement of the features of the mammogram giving an indication of cancer. In this paper, contrast limited adaptive histogram equalization (CLAHE) and entropy-based intuitionistic fuzzy method are anticipated for improving the contrast of digital mammogram images. To validate the efficacy of the proposed algorithm over type II fuzzy set-based techniques, subjective, quantitative and visual evaluation is done on publicly available MIAS database. Experimental results prove that the proposed technique gives better visual quality. It provides high values of subjective and quantitative metrics compared to several states of art algorithms.