A Robust Grey Wolf-based Deep Learning for Brain Tumour Detection in MR Images

In recent times, the detection of brain tumour is a common fatality in the field of the health community. Generally, the brain tumor is an abnormal mass of tissue where the cells grow up and increase uncontrollably, apparently unregulated by mechanisms that control cells. A number of techniques have been developed so far; however, the time consumption in detecting brain tumor is still a challenge in the field of image processing.  This paper intends to propose a new detection model even accurately. The model includes certain processes like Preprocessing, Segmentation, Feature Extraction and Classification. Particularly, two extreme processes like contrast enhancement and skull stripping are processed under initial phase, in the segmentation process, this paper uses Fuzzy Means Clustering (FCM) algorithm. Both Gray Level Co-occurrence Matrix (GLCM) as well as Gray-Level Run-Length Matrix (GRLM) features are extracted in feature extraction phase. Moreover, this paper uses Deep Belief Network (DBN) for classification. The DBN is integrated with the optimization approach, and hence this paper introduces the optimized DBN, for which Grey Wolf Optimization (GWO) is used here.  The proposed model is termed as GW-DBN model. The proposed model compares its performance over other conventional methods in terms of Accuracy, Specificity, Sensitivity, Precision, Negative Predictive Value (NPV), F1Score and Matthews Correlation Coefficient (MCC), False negative rate (FNR), False positive rate (FPR) and False Discovery Rate (FDR), and proven the superiority of proposed work.

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Brain Neoplasm Classification & Detection of Accuracy on MRI Images

The abnormal, uncontrolled cell growth in the brain, commonly known n as a brain tumor, can lead to immense pressure on the various nerves and blood vessels, causing irreversible harm to the body. Early detection of brain tumors is the key to avoiding such compilations. Tumour detection can be done through various advanced Machine Learning and Image Processing algorithms. Mind Brain tumors have demonstrated testing to treat, to a great extent inferable from the organic qualities of these diseases, which frequently plan to restrict progress. To begin with, by invading one of the body's most significant organs, these growths are much of the time situated past the compass of even the most gifted neurosurgeon. These cancers are likewise situated behind the blood-cerebrum boundary (BBB), a tight intersection and transport proteins that shield fragile brain tissues from openness to factors in the overall flow, subsequently obstructing openness to foundational chemotherapy [6,7]. Besides, the interesting formative, hereditary, epigenetic and micro environmental elements of the cerebrum much of the time render these tumors impervious to ordinary and novel medicines. These difficulties are accumulated by the uncommonness of cerebrum growths comparative with numerous different types of disease, restricting the degree of subsidizing and interest from the drug business and drawing in a moderately little and divided research local area

Segmentation Method for Pathological Brain Tumor and Accurate Detection using MRI

Image segmentation is challenging task in field of medical image processing. Magnetic resonance imaging is helpful to doctor for detection of human brain tumor within three sources of images (axil, corneal, sagittal). MR images are nosier and detection of brain tumor location as feature is more complicated. Level set methods have been applied but due to human interaction they are affected so appropriate contour has been generated in discontinuous regions and pathological human brain tumor portion highlighted after applying binarization, removing unessential objects; therefore contour has been generated. Then to classify tumor for segmentation hybrid Fuzzy K Mean-Self Organization Mapping (FKM-SOM) for variation of intensities is used. For improved segmented accuracy, classification has been performed, mainly features are extracted using Discrete Wavelet Transformation (DWT) then reduced using Principal Component Analysis (PCA). Thirteen features from every image of dataset have been classified for accuracy using Support Vector Machine (SVM) kernel classification (RBF, linear, polygon) so results have been achieved using evaluation parameters like Fscore, Precision, accuracy, specificity and recall

Enhancing Alzheimer Disease Segmentation through Adaptively Regularized Weighted Kernel-Based Clustering

Image segmentation is important in image analysis because it helps to locate objects and boundaries within a picture. This study offers Adaptively Regularized Weighted Kernel-Based Clustering (ARWKC), a unique segmentation technique built exclusively for recovering brain tissue from medical pictures. The proposed approach incorporates adaptive regularization and weighted kernel-based clustering techniques to increase the accuracy and resilience of brain tissue segmentation. The picture is initially preprocessed with the ARWKC method to improve its quality and eliminate any noise or artifacts. The adaptive regularization method is then utilized to effectively deal with the visual variation of brain tissue in clinical images. This adaptive regularization contributes to more accurate and consistent segmentation outcomes. The weighted kernel-based clustering method is then used to find and group pixels with comparable properties, with a focus on brain tissue areas. This clustering approach employs a weighted kernel function that takes into account both geographical closeness and pixel intensities, allowing the algorithm to capture local picture features and improve segmentation accuracy. Extensive experiments were conducted on a collection of medical images to evaluate the efficacy of the ARWKC algorithm. The well-known k-means clustering method, often used in image segmentation applications, was utilized as a benchmark for comparison. In terms of accuracy and resilience for brain tissue segmentation, the experimental findings showed that the ARWKC method surpasses the k-means clustering approach

Cuckoo lévy flight with otsu for image segmentation in cancer detection

Detecting cancer cells from computed tomography (CT), magnetic resonance imaging (MRI) or mammogram scan images is a challenging task as the images are black and white and the neighbouring organs tend to be separated by edges with smooth varying intensity. On top of that, medical images segmentation is challenging due to the presence of weakly correlated and ambiguous multiple regions of interest. A few bio-inspired algorithms were developed to efficiently generate optimum threshold values for the process of segmenting such images. Their exhaustive search nature makes them computationally expensive when extended to multilevel thresholding, thus, this research is keen to solve the optimum threshold problems. This research propose an enhancement of image segmentation algorithms based on Otsu’s method by incorporating Cuckoo Search (CS) method for Lévy flight generation while simultaneously modifying and optimizing it to work on CT, MRI or mammogram image scanners, specifically to detect breast cancer. The performance of the proposed Otsu’s method with CS algorithm was compared with other bio-inspired algorithms such as Otsu with Particle Swarm Optimization (PSO) and Otsu with Darwinian Particle Swarm Optimization (DPSO). The experimental results were validated by measuring the peak signal-to-noise ratio (PNSR), mean squared error (MSE), feature similarity index (FSIM) and CPU running time for all cases investigated. The proposed Otsu’s method with CS algorithm experimental results achieved an average of 231.52 of MSE, 24.60 of PNSR, 0.93 of FSIM and 3.36 second of CPU running time. The method evolved to be more promising and computationally efficient for segmenting medical images. It is expected that the experiment results will benefit those in the areas of computer vision, remote sensing and image processing application

Modified Social Group Optimization Based Deep Learning Techniques for Automation of Brain Tumor Detection–A Health Care 4.0 Application

Now-a-days, Segmentation is essential in diagnosing severe diseases wherever there is a scope for image processing. In this work, hybridization of most popular and metaheuristic algorithms with Conventional Neural Network (CNN) has been proposed. As a part of the study, jelly fish and Modified Social Group Optimization Algorithms (MSGOA) are used. The CNN weights and the corresponding hyper parameters are modified or designed with the help of the respective metaheuristic approach of the algorithm. This certainly improved the efficiency of the segmentation which is measured in several metrics of bio-medical image processing. The accuracy, loss, Intersection over Union (IoU) are some of those metrics which are employed in this study for better understanding of the algorithm’s effectiveness. Further the detection process is simulated consuming 100 iterations uniformly in either of the algorithms. The proposed methodology has efficiently segmented the tumor portion. The simulation has been carried out in MATLAB and the results are presented in terms of computed metrics, convergence plots and segmented images