Computer-Aided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms

Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron

BACKGROUND

The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

OBJECTIVE

A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented.

METHODS

The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features.

RESULTS

Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered.

CONCLUSION

The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%.

Bioinspired Photoreceptors with Neural Network for Recognition and Classification of Sign Language Gesture

This work addresses the design and implementation of a novel PhotoBiological Filter Classifier (PhBFC) to improve the accuracy of a static sign language translation system. The captured images are preprocessed by a contrast enhancement algorithm inspired by the capacity of retinal photoreceptor cells from mammals, which are responsible for capturing light and transforming it into electric signals that the brain can interpret as images. This sign translation system not only supports the effective communication between an agent and an operator but also between a community with hearing disabilities and other people. Additionally, this technology could be integrated into diverse devices and applications, further broadening its scope, and extending its benefits for the community in general. The bioinspired photoreceptor model is evaluated under different conditions. To validate the advantages of applying photoreceptors cells, 100 tests were conducted per letter to be recognized, on three different models (V1, V2, and V3), obtaining an average of 91.1% of accuracy on V3, compared to 63.4% obtained on V1, and an average of 55.5 Frames Per Second (FPS) in each letter classification iteration for V1, V2, and V3, demonstrating that the use of photoreceptor cells does not affect the processing time while also improving the accuracy. The great application potential of this system is underscored, as it can be employed, for example, in Deep Learning (DL) for pattern recognition or agent decision-making trained by reinforcement learning, etc.

A classification framework using filter–wrapper based feature selection approach for the diagnosis of congenital heart failure

Premature mortality from cardiovascular disease can be reduced with early detection of heart failure by analysing the patients’ risk factors and assuring accurate diagnosis. This work proposes a clinical decision support system for the diagnosis of congenital heart failure by utilizing a data pre-processing approach for dealing missing values and a filter-wrapper based method for selecting the most relevant features. Missing values are imputed using a missForest method in four out of eight heart disease datasets collected from the Machine Learning Repository maintained by University of California, Irvine. The Fast Correlation Based Filter is used as the filter approach, while the union of the Atom Search Optimization Algorithm and the Henry Gas Solubility Optimization represent the wrapper-based algorithms, with the fitness function as the combination of accuracy, G-mean, and Matthew’s correlation coefficient measured by the Support Vector Machine. A total of four boosted classifiers namely, XGBoost, AdaBoost, CatBoost, and LightGBM are trained using the selected features. The proposed work achieves an accuracy of 89%, 84%, 83%, 80% for Heart Failure Clinical Records, 81%, 80%, 83%, 82% for Single Proton Emission Computed Tomography, 90%, 82%, 93%, 80% for Single Proton Emission Computed Tomography F, 80%, 80%, 81%, 80% for Statlog Heart Disease, 80%, 85%, 83%, 86% for Cleveland Heart Disease, 82%, 85%, 85%, 82% for Hungarian Heart Disease, 80%, 81%, 79%, 82% for VA Long Beach, 97%, 89%, 98%, 97%, for Switzerland Heart Disease for four classifiers respectively. The suggested technique outperformed the other classifiers when evaluated against Random Forest, Classification and Regression Trees, Support Vector Machine, and K-Nearest Neighbor.

Diagnosis of Pulmonary Edema and covid-19 from CT slices using squirrel search algorithm, support vector machine and back propagation neural network

A Computer Aided Diagnosis (CAD) framework to diagnose Pulmonary Edema (PE) and covid-19 from the chest Computed Tomography (CT) slices have been developed and implemented in this work. The lung tissues have been segmented using Otsu’s thresholding method. The Regions of Interest (ROI) considered in this work were edema lesions and covid-19 lesions. For each ROI, the edema lesions and covid-19 lesions were elucidated by an expert radiologist, followed by texture and shape extraction. The extracted features were stored as feature vectors. The feature vectors were split into train and test set in the ratio of 80 : 20. A wrapper based feature selection approach using Squirrel Search Algorithm (SSA) with the Support Vector Machine (SVM) classifier’s accuracy as the fitness function was used to select the optimal features. The selected features were trained using the Back Propagation Neural Network (BPNN) classifier. This framework was tested on a real-time PE and covid-19 dataset. The BPNN classifier’s accuracy with SSA yielded 88.02%, whereas, without SSA it yielded 83.80%. Statistical analysis, namely Wilcoxon’s test, Kendall’s Rank Correlation Coefficient test and Mann Whitney U test were performed, which indicates that the proposed method has a significant impact on the accuracy, sensitivity and specificity of the novel dataset considered. Comparative experimentations of the proposed system with existing benchmark ML classifiers, namely Cat Boost, Ada Boost, XGBoost, RBF SVM, Poly SVM, Sigmoid SVM and Linear SVM classifiers demonstrate that the proposed system outperforms the benchmark classifiers’ results.

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

There is a need for some techniques to solve various problems in today's computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study's primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.

Deep Learning for COVID-19 Diagnosis from CT Images

COVID-19, an infectious coronavirus disease, caused a pandemic with countless deaths. From the outset, clinical institutes have explored computed tomography as an effective and complementary screening tool alongside the reverse transcriptase-polymerase chain reaction. Deep learning techniques have shown promising results in similar medical tasks and, hence, may provide solutions to COVID-19 based on medical images of patients. We aim to contribute to the research in this field by: (i) Comparing different architectures on a public and extended reference dataset to find the most suitable; (ii) Proposing a patient-oriented investigation of the best performing networks; and (iii) Evaluating their robustness in a real-world scenario, represented by cross-dataset experiments. We exploited ten well-known convolutional neural networks on two public datasets. The results show that, on the reference dataset, the most suitable architecture is VGG19, which (i) Achieved 98.87% accuracy in the network comparison; (ii) Obtained 95.91% accuracy on the patient status classification, even though it misclassifies some patients that other networks classify correctly; and (iii) The cross-dataset experiments exhibit the limitations of deep learning approaches in a real-world scenario with 70.15% accuracy, which need further investigation to improve the robustness. Thus, VGG19 architecture showed promising performance in the classification of COVID-19 cases. Nonetheless, this architecture enables extensive improvements based on its modification, or even with preprocessing step in addition to it. Finally, the cross-dataset experiments exposed the critical weakness of classifying images from heterogeneous data sources, compatible with a real-world scenario.

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).