Ultrasound image segmentation through deep learning based improvised U-Net

Shenoy, Nayana R.; Jatti, Anand

doi:10.11591/ijeecs.v21.i3.pp1424-1434

Cited by 13 publications

(9 citation statements)

References 21 publications

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“…Each node in the tree splits the data while the leaves represent the choices or the outcome decisions [34]. Resilient propagation (RPROP) is considered a robust machine learning scheme that adopts the local gradient information for the weight step [35], [36].…”

Section: Classificationmentioning

confidence: 99%

Network intrusion detection system: machine learning approach

Jaradat

Barhoush²,

Easa³

2022

IJEECS

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The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. Recently, intrusion detection systems employ machine learning techniques, due to the fact that machine learning techniques proved to have the ability of learning and adapting in addition to allowing a prompt response. This work proposes a model for intrusion detection and classification using machine learning techniques. The model first acquires the data set and transforms it in the proper format, then performs feature selection to pick out a subset of attributes that worth being considered. After that, the refined data set was processed by the Konstanz information miner (KNIME). To gain better performance and a decent comparative analysis, three different classifiers were applied. The anticipated classifiers have been executed and assessed utilizing the KNIME analytics platform using (CICIDS2017) datasets. The experimental results showed an accuracy rate ranging between (98.6) as the highest obtained while the average was (90.59%), which was satisfying compared to other approaches. The gained statistics of this research inspires the researchers of this field to use machine learning in cyber security and data analysis and build intrusion detection systems with higher accuracy.

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Section: Classificationmentioning

confidence: 99%

Network intrusion detection system: machine learning approach

Jaradat

Barhoush²,

Easa³

2022

IJEECS

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“…The set of candidate solutions (C) is generated from the decision trees (DT) each iteration the ideal candidate solution is treated as a best obtained or the nearly optimum solution. There are L image dataset in the decision tree, the candidate solution C is represented in (5),…”

Section: Initialization Phasementioning

confidence: 99%

“…But the rate of MRI is high and it is difficult to differentiate the normal lumps Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752 ❒ 581 and cancerous lumps from the breast MRI. The breast ultrasound produces less accurate results for patients with dense breast [5]. The result of images is based on the expert who is taking the ultrasound for the patient.…”

Section: Introductionmentioning

confidence: 99%

Automated breast cancer detection system from breast mammogram using deep neural network

Chittineni¹,

Edara²

2022

IJEECS

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<p>All over the world breast cancer is a major disease which mostly affects the women and it may also cause death if it is not diagnosed in its early stage. But nowadays, several screening methods like magnetic resonance imaging (MRI), ultrasound imaging, thermography and mammography are available to detect the breast cancer. In this article mammography images are used to detect the breast cancer. In mammography image the cancerous lumps/microcalcifications are seen to be tiny with low contrast therefore it is difficult for the doctors/radiologist to detect it. Hence, to help the doctors/radiologist a novel system based on deep neural network is introduced in this article that detects the cancerous lumps/microcalcifications automatically from the mammogram images. The system acquires the mammographic images from the mammographic image analysis society (MIAS) data set. After pre-processing these images by 2D median image filter, cancerous features are extracted from the images by the hybridization of convolutional neural network with rat swarm optimization algorithm. Finally, the breast cancer patients are classified by integrating random forest with arithmetic optimization algorithm. This system identifies the breast cancer patients accurately and its performance is relatively high compared to other approaches.</p>

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“…As a primary step in preprocessing, image enhancement is carried out to improve the contrast and vividness of the features in the fundus images [7]. Changes in the elements and textures of objects in the enhanced fundus images provide an essential biomarker for carrying out comprehensive clinical analysis of the region of interest (ROI) towards pre- [8]. There isa range of preprocessing approaches given in the literature for enhancing the quality of the fundus images.…”

Section: Introductionmentioning

confidence: 99%

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

Krishnegowda¹,

Ganesan²

2022

IJECE

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<p>Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification.</p>

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Ultrasound image segmentation through deep learning based improvised U-Net

Cited by 13 publications

References 21 publications

Network intrusion detection system: machine learning approach

Network intrusion detection system: machine learning approach

Automated breast cancer detection system from breast mammogram using deep neural network

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

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