Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet

Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull.

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“…The mathematical expression for these measures is presented in equations ( 15 – 18 ) [ 25 , 51 – 55 ].

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Section: Methodsmentioning

confidence: 99%

Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

Krishnamoorthy

Zhang

Kadry

et al. 2023

Computational Intelligence and Neuroscience

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“…Like all such diseases, early detection and treatment are necessary in order to reduce the impact of the diseases. The authors in [ 21 ] propose a convolutional neural network- (CNN-) based framework (CNN) segmentation scheme for the extraction of MS lesion from a 2D brain MRI slice. They further implemented the VGG-UNet scheme in order to achieve a better MS detection.…”

Section: Related Workmentioning

confidence: 99%

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Aamir

Rahim

Aamir

et al. 2022

Computational and Mathematical Methods in Medicine

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Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data.

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“…The earlier studies in the literature confirm that renal CT (RCT)-based kidney detection is a recommended procedure to precisely detect kidney abnormality during the disease screening process. Usually, the RCT is collected as a three-dimensional (3D) image, and then, a 3D to 2D conversion is employed to reduce the computation complexity during the RCT analysis ( 4 , 5 ). The axial-plane 2D slices are commonly adopted in the literature, and it helps to provide the necessary information about abdominal conditions, including kidney health.…”

Section: Introductionmentioning

confidence: 99%

A framework to distinguish healthy/cancer renal CT images using the fused deep features

Rajinikanth

Vincent

Srinivasan

et al. 2023

Front. Public Health

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IntroductionCancer happening rates in humankind are gradually rising due to a variety of reasons, and sensible detection and management are essential to decrease the disease rates. The kidney is one of the vital organs in human physiology, and cancer in the kidney is a medical emergency and needs accurate diagnosis and well-organized management.MethodsThe proposed work aims to develop a framework to classify renal computed tomography (CT) images into healthy/cancer classes using pre-trained deep-learning schemes. To improve the detection accuracy, this work suggests a threshold filter-based pre-processing scheme, which helps in removing the artefact in the CT slices to achieve better detection. The various stages of this scheme involve: (i) Image collection, resizing, and artefact removal, (ii) Deep features extraction, (iii) Feature reduction and fusion, and (iv) Binary classification using five-fold cross-validation.Results and discussionThis experimental investigation is executed separately for: (i) CT slices with the artefact and (ii) CT slices without the artefact. As a result of the experimental outcome of this study, the K-Nearest Neighbor (KNN) classifier is able to achieve 100% detection accuracy by using the pre-processed CT slices. Therefore, this scheme can be considered for the purpose of examining clinical grade renal CT images, as it is clinically significant.

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Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet

Cited by 16 publications

References 29 publications

Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

A framework to distinguish healthy/cancer renal CT images using the fused deep features

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