Brain Tumor Segmentation Techniques: A Review

Image processing is used widely in solving a variety of problems. The important and complex phase of image processing is image segmentation. This paper provides a brief description on some of the segmentation algorithms specifically on brain tumor MR Images. Later in this paper, simple comparisons are made between the listed algorithms. This work helps in understanding some of the existing brain MR Image segmentation algorithms better

Survey analysis for optimization algorithms applied to electroencephalogram

This paper presents a survey for optimization approaches that analyze and classify Electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions

Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

VGG19+CNN: Deep Learning-Based Lung Cancer Classification with Meta-Heuristic Feature Selection Methodology

Lung illnesses are lung-affecting illnesses that harm the respiratory mechanism. Lung cancer is one of the major causes of death in humans internationally. Advance diagnosis could optimise survivability amongst humans. This remains feasible to systematise or reinforce the radiologist for cancer prognosis. PET and CT scanned images can be used for lung cancer detection. On the whole, the CT scan exhibits importance on the whole and functions as a comprehensive operation in former cancer prognosis. Thus, to subdue specific faults in choosing the feature and optimise classification, this study employs a new revolutionary algorithm called the Accelerated Wrapper-based Binary Artificial Bee Colony algorithm (AWBABCA) for effectual feature selection and VGG19+CNN for classifying cancer phases. The morphological features will be extracted out of the pre-processed image; next, the feature or nodule related to the lung that possesses a significant impact on incurring cancer will be chosen, and for this intention, herein AWBABCA has been employed. The chosen features will be utilised for cancer classification, facilitating a great level of strength and precision. Using the lung dataset to do an experimental evaluation shows that the proposed classifier got the best accuracy, precision, recall, and f1-score

Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier

Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD) of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI) is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach. In this study, we propose a new model that includes Wiener filtering for noise reduction, 2D-discrete wavelet transform (2D-DWT) for feature extraction, probabilistic principal component analysis (PPCA) for dimensionality reduction, and a random subspace ensemble (RSE) classifier along with the K-nearest neighbors (KNN) algorithm as a base classifier to classify brain images as pathological or normal ones. The proposed methods provide a significant improvement in classification results when compared to other studies. Based on 5×5 cross-validation (CV), the proposed method outperforms 21 state-of-the-art algorithms in terms of classification accuracy, sensitivity, and specificity for all four datasets used in the study