Image fusion practice to improve the ischemic-stroke-lesion detection for efficient clinical decision making

Hemanth, D. Jude; Rajinikanth, V.; Rao, V. Seshagiri; Mishra, Samaresh; Hannon, Naeem M. S.; Rajangam, Vijayarajan; Arunmozhi, S.

doi:10.1007/s12065-020-00551-0

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…+ere are many challenges in automatic segmentation problems, and their use in practice is quite limited due to the difficulty of correctly validating the predictions made by these tools. In this memory, the effectiveness of different segmentation methods for this type of image will be studied, proposing the use of convolutional neural networks for the segmentation of lesions and comparing them with traditional segmentation methods, analyzing the advantages and disadvantages that each of these methods they bring into practice [5].…”

Section: Introductionmentioning

confidence: 99%

Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging

Hamad

Jaber

Ahmed

et al. 2022

Advances in Materials Science and Engineering

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Magnetic Resonance Imaging to detect its lesions is used to diagnose multiple sclerosis. Experts usually perform this detection process manually, but there is interest in automating it to speed up the diagnosis and monitoring of this disease. A variety of automatic image segmentation methods have been proposed to quickly detect these lesions. A Gaussian Mixture Model is first constructed to identify outliers in each image. Then, using a set of rules based on expert knowledge of multiple sclerosis lesions, those outliers of the model that do not match the lesions' characteristics are discarded. Furthermore, segmented lesions usually correspond to gray matter-rich brain regions. In some cases, false positives can be detected, but the rules used cannot eliminate all errors without jeopardizing the segmentation’s quality. The second method involves training a convolutional neural network (CNN) that can segment lesions based on a set of training images. This technique can learn a set of filters that, when applied to small sections of an image called “patches,” produce a set of characteristics that can be used to classify each voxel of the image as a lesion or healthy tissue. On the other hand, the results show that the networks are capable of producing results in the worked database comparable to those produced by the algorithms in the literature.

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Section: Introductionmentioning

confidence: 99%

Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging

Hamad

Jaber

Ahmed

et al. 2022

Advances in Materials Science and Engineering

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“…The 2D test image was available in a dimension; pixels and image resizing were employed to obtain pixels. Other related information on ISLES2015 can be found in [17][18][19]. features are reduced by using three numbers of fully connected (FC) layers, such as FC FC2, and FC3, with 50% dropout in every stage.…”

Section: Image Databasementioning

confidence: 99%

“…The work by Rajinikanth and Satapathy [16] presented joint thresholding and segmentation-based ISL assessment, and a similar attempt was presented in the research by Lin et al [17]. The recent work by Hemanth et al [18] implemented a multi-modality fusion-based ISL examination. The review by Zhang et al [19] confirmed the following limitations in earlier works: (i) modalityspecific detection, (ii) in most of the modalities, automated extraction and evaluation are quite difficult, and (iii) less detection accuracy for T1 modality case.…”

Section: Introductionmentioning

confidence: 96%

Deep Learning Framework to Detect Ischemic Stroke Lesion in Brain MRI Slices of Flair/DW/T1 Modalities

Rajinikanth

Aslam

Kadry³

2021

Symmetry

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Ischemic stroke lesion (ISL) is a brain abnormality. Studies proved that early detection and treatment could reduce the disease impact. This research aimed to develop a deep learning (DL) framework to detect the ISL in multi-modality magnetic resonance image (MRI) slices. It proposed a convolutional neural network (CNN)-supported segmentation and classification to execute a consistent disease detection framework. The developed framework consisted of the following phases; (i) visual geometry group (VGG) developed VGG16 scheme supported SegNet (VGG-SegNet)-based ISL mining, (ii) handcrafted feature extraction, (iii) deep feature extraction using the chosen DL scheme, (iv) feature ranking and serial feature concatenation, and (v) classification using binary classifiers. Fivefold cross-validation was employed in this work, and the best feature was selected as the final result. The attained results were separately examined for (i) segmentation; (ii) deep-feature-based classification, and (iii) concatenated feature-based classification. The experimental investigation is presented using the Ischemic Stroke Lesion Segmentation (ISLES2015) database. The attained result confirms that the proposed ISL detection framework gives better segmentation and classification results. The VGG16 scheme helped to obtain a better result with deep features (accuracy > 97%) and concatenated features (accuracy > 98%).

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“…N OW deep learning has been used in many fields, especially in health care to improve medical diagnosis [1]. Constructing deep learning models often entails large quantities of data.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Image Gradient Inversion Against Federated Learning

Li¹,

Wang²,

Chen³

et al. 2022

Preprint

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In order to preserve data privacy while fully utilizing data from different owners, federated learning is believed to be a promising approach in recent years. However, aiming at federated learning in the image domain, gradient inversion techniques can reconstruct the input images on pixel-level only by leaked gradients, without accessing the raw data, which makes federated learning vulnerable to the attacks. In this paper, we review the latest advances of image gradient inversion techniques and evaluate the impact of them to federated learning from the attack perspective. We use eight models and four datasets to evaluate the current gradient inversion techniques, comparing the attack performance as well as the time consumption. Furthermore, we shed light on some important and interesting directions of gradient inversion against federated learning.<br>

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Image fusion practice to improve the ischemic-stroke-lesion detection for efficient clinical decision making

Cited by 9 publications

References 28 publications

Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging

Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging

Deep Learning Framework to Detect Ischemic Stroke Lesion in Brain MRI Slices of Flair/DW/T1 Modalities

A Survey of Image Gradient Inversion Against Federated Learning

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