Classification of Pneumonia in Thoracic X-Ray images based on texture characteristics using the MLP (Multi-Layer Perceptron) method

One of the diseases that attack the lungs is pneumonia. This disease can attack someone with a weak immune system. Pneumonia is inflammation of the lungs that can be caused by pathogens, such as bacteria, viruses, and fungi. The purpose of this study was to classify fungal pneumonia, bacterial pneumonia, and lipoid pneumonia based on texture characteristics and the MLP method using machine learning WEKA. The method in this study has three stages including pre-processing, extraction of texture features consisting of Histogram and GLCM, and classification using the MLP (Multi Layer Perceptron) method. The results of the texture feature extraction showed that the three types of pneumonia were: lipoid pneumonia with brightness, sharp contrast random distribution on correlation characteristics, bacterial pneumonia with high brightness, high contrast random distribution on energy characteristics, and fungal pneumonia with brightness, sharp contrast, random distribution of homogeneity attributes. The third similarity of pneumonia is in the gray level that collects each other in the middle. The method used in this study resulted in the same accuracy, sensitivity, and specificity, namely 100%. The image classification in this study shows the success of the texture features and the MLP method in classifying pneumonia images accurately so that they can be used as additional tools that make it easier for medical experts.   ©2020 JNSMR UIN Walisongo. All rights reserved.

Classification of Pneumonia in Thoracic X-Ray images based on texture characteristics using the MLP (Multi-Layer Perceptron) method

One of the diseases that attack the lungs is pneumonia. This disease can attack someone with a weak immune system. Pneumonia is inflammation of the lungs that can be caused by pathogens, such as bacteria, viruses, and fungi. The purpose of this study was to classify fungal pneumonia, bacterial pneumonia, and lipoid pneumonia based on texture characteristics and the MLP method using machine learning WEKA. The method in this study has three stages including pre-processing, extraction of texture features consisting of Histogram and GLCM, and classification using the MLP (Multi Layer Perceptron) method. The results of the texture feature extraction showed that the three types of pneumonia were: lipoid pneumonia with brightness, sharp contrast random distribution on correlation characteristics, bacterial pneumonia with high brightness, high contrast random distribution on energy characteristics, and fungal pneumonia with brightness, sharp contrast, random distribution of homogeneity attributes. The third similarity of pneumonia is in the gray level that collects each other in the middle. The method used in this study resulted in the same accuracy, sensitivity, and specificity, namely 100%. The image classification in this study shows the success of the texture features and the MLP method in classifying pneumonia images accurately so that they can be used as additional tools that make it easier for medical experts.   ©2020 JNSMR UIN Walisongo. All rights reserved.

Detection of Pathologies in X-Rays Based on a Deep Learning Framework

The diagnostic process of respiratory diseases requires experience and skills to assess the different pathologies that patients may develop. Unfortunately, the lack of qualified radiologists is a global problem that limits respiratory diseases diagnosis. Therefore, it will be useful to have a tool that minimizes errors and workload, improves efficiency, and speeds up the diagnostic process in order to provide a better healthcare service to the community. This research proposes a methodology to detect pathologies by using deep learning architectures. The present proposal is divided into three types of experiments. The first one evaluates the performance of feature descriptors such as SIFT, SURF, and ORB in medical images with machine learning models as an introduction to the last experiment. The second one evalu ates the performance of deep learning architectures such as ResNet50, Alexnet, VGG16, and LeNet. Finally, the third one evaluates the combination of deep learning and machine learn ing classifiers. Furthermore, a novel chest X-ray dataset called PathX_Chest, which contains 2,200 images of ten different classes, is presented. In contrast with the state of the art, good results were obtained in the three different approaches. However, the best performance was achieved by combining deep learning and machine learning: a 99.99 % accuracy was obtained with the combination of ResNet50 and SVM classifier. This methodology may be used to develop a CAD system to help radiologists have a second opinion and a support during the diagnostic procedur

Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system

Chest infection is a major health threat in most regions of the world. It is claimed to be one of the top causes of postoperative death after fragility hip fractures, according to a study presented in 2011. With the invention of deep learning in machine learning, implementation in Computer Aided Diagnosis system which utilizes deep neural networks for learning, classification, generation and even clustering has allowed X-ray image classification to be more accurate. The improvement in medical image classification using transfer learning is further studied. In this thesis, a novel deep neural network model which is composed of two Convolutional Neural Networks (CNNs) with different depth of weight layers, where the prediction probabilities for all CNNs are fused to the voting system for chest X-ray image classification is proposed and presented. The performance and accuracy of several existing deep learning model are investigated and compared to the proposed model. The outcome of this work, we successfully classified chest infection in chest X-ray images using the proposed model with overall accuracy of 83.69%

Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

A self-learning disease detection model will be useful for identifying skin infections in suspected individuals using skin images of infected patients. To detect skin diseases, some AI-based bioinspired models employ skin images. Skin infection is a common problem that is currently faced due to various reasons, such as food, water, environmental factors, and many others. Skin infections such as psoriasis, skin cancer, monkeypox, and tomato flu, among others, have a lower death rate but a significant impact on quality of life. Neural Networks (NNs) and Swarm intelligence (SI) based approaches are employed for skin disease diagnosis and classification through image processing. In this paper, the convolutional neural networks-based Cuckoo search algorithm (CNN-CS) is trained using the well-known multi-objective optimization technique cuckoo search. The performance of the suggested CNN-CS model is evaluated by comparing it with three commonly used metaheuristic-based classifiers: CNN-GA, CNN-BAT, and CNN-PSO. This comparison was based on various measures, including accuracy, precision, recall, and F1-score. These measures are calculated using the confusion matrices from the testing phase. The results of the experiments revealed that the proposed model has outperformed the others, achieving an accuracy of 97.72%

Advances in Management of Voice and Swallowing Disorders

Special Issue “Advances in Management of Voice and Swallowing Disorders” is dedicated to innovations in screening and assessment and the effectiveness of interventions in both dysphonia and dysphagia. In contemporary practice, novel techniques have been introduced in diagnostics and rehabilitative interventions (e.g., machine learning, electrical stimulation). Similarly, advancements in methodological approaches to validate measures have been introduced (e.g., item response theory using Rasch analysis), prompting the need to develop new, robust measures for use in clinics and intervention studies. Against this backdrop, this Special Issue focuses on studies aiming to improve early diagnostics of laryngological disorders and its management. This issue also welcomes the submission of studies on diagnostic accuracy and psychometrics performance of existing and newly developed measures. This includes but is not limited to studies investigating screening tools with sound diagnostic accuracy and robust psychometric properties. Furthermore, interventions with high levels of evidence in relation to clinical outcome using robust methodology (e.g., sophisticated meta-analytic approaches) are of great interest. This issue provides an overview of the latest advances in voice and swallowing disorders