Convolutional Neural Networks on Assembling Classification Models to Detect Melanoma Skin Cancer

Huerta, Hugo Vega; Villanueva-Alarcón, Renzo; Mauricio, David; Calderon-Vilca, Hugo D.; Rodriguez, Diego; Rodríguez, Ciro Rodríguez

doi:10.3991/ijoe.v18i14.34435

Cited by 15 publications

(11 citation statements)

References 26 publications

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“…Medical imaging analysis plays a critical role in diagnosing disease such as diabetic retinopathy [19], skin cancer [20] and lung disease [21]. In literature, several studies have been reported for automatic segmentation of myocardial scar and edema.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Shaaf

Jamil²,

Ambar³

2023

Int. J. Onl. Eng.

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Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing such diseases. The segmentation of the left ventricle (LV) and myocardium from MRI images is vital in detecting MI disease at its early stages. The automatic segmentation of LV is still challenging due to the complex structures of MRI images, inhomogeneous LV shape and moving organs around the LV, such as the lungs and diaphragm. Thus, this study proposed a convolutional neural network (CNN) model for LV and myocardium segmentation to detect MI. The layers selection and hyper-parameters fine-tuning were applied before the training phase. The model showed robust performance based on the evaluation metrics such as accuracy, sensitivity, specificity, dice score coefficient (DSC), Jaccard index and intersection over union (IOU) with values of 0.86, 0.91, 0.84, 0.81, 0.69 and 0.83, respectively.

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Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Shaaf

Jamil²,

Ambar³

2023

Int. J. Onl. Eng.

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“…Among such processes, the RRP aims to review incoming requests, validate data and business rules, register data through technological tools and register requests to proceed to their referral through the corresponding channel. Intelligent Processes are also considered those systems that, from the processing of data and images, can detect behavioral patterns or trends [17][18][19][20][21][22][23].…”

Section: Business Processmentioning

confidence: 99%

Robotic Automation of Application Registry Processes in State Organizations

De-La-Cruz-VDV,

Carhuavilca-Carlos,

Vega-Huerta

et al. 2023

IJEETC

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Robotic Process Automation (RPA) is the use of software in bots to perform routine tasks that replicate the interactions of a human user with a graphical interface and is currently positioned as a new innovative approach to lighten the high demand for attention requests. This research presents a set of ordered actions to follow, with the purpose of developing an RPA solution applied to a process of the administrative-financial area, which manages to detail each step in a clear and precise way to be performed. For its creation, the Waterfall methodology was used, applied to the development of the RPA solution for the case of an application registration process of a state agency, which fits with the type of user areas where these of automation are usually applied. As a result, it was possible to describe and analyze the process to establish the design to be used in the implementation of the RPA solution, thus determining a procedure to be replicated in cases of similar characteristics.

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“…This approach, however, is constrained by its inability to ensure continuous monitoring. The advent of Internet of Things (IoT) and Digital Image Processing (DIP) technologies has demonstrated their efficacy, as they find application in various domains to support, facilitate, and resolve diverse challenges [17], [18], [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Saeliw,

Hualkasin,

Puttinaovarat

2024

TEM Journal

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One of the challenges encountered by farmers engaged in livestock farming is the menace posed by stray or ownerless dogs, causing harm to the animals being raised on the farm. This not only adversely affects the health of the animals but also impacts the overall cost associated with their upbringing. Consequently, this research introduces the development of a sophisticated system aimed at preventing threats and intrusions by dogs that pose harm to farm animals. The system leverages Internet of Things (IoT) technology and employs Machine Learning algorithms, specifically Convolutional Neural Network, for real-time tracking and monitoring. The research findings reveal that the developed system demonstrates a high level of efficiency, swiftly and accurately classifying animals entering areas equipped with cameras, achieving an impressive accuracy rate of 92.54%. Furthermore, the system is equipped to promptly notify users and emit deterrent sounds to repel dogs entering the monitored area, enhancing its effectiveness in safeguarding livestock and optimizing farm management practices.

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Convolutional Neural Networks on Assembling Classification Models to Detect Melanoma Skin Cancer

Cited by 15 publications

References 26 publications

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Robotic Automation of Application Registry Processes in State Organizations

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

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