Enhancing Soil-Transmitted Helminth Detection in Microscopic Images Using the Chain Code for Object Feature Extraction

Malik, Rio Andika; Frimadani, Marta Riri; Putra, Dwipa Junika

doi:10.25008/ijadis.v4i2.1305

Cited by 1 publication

(4 citation statements)

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“…The following crucial elements stand for open issues and areas that need improvement. Even though STH can be detected using CNN, our study still faces difficulties in automatically extracting pertinent features from microscopic pictures [22]. Improving the network's capacity to recognize and extract minor but important elements on its own might improve STH classification accuracy even more.…”

Section: Methodsmentioning

confidence: 99%

“…In the context of STH classification, it is necessary to enhance the interpretability and explainability of CNN decisions because they function as intricate, opaque models. More confidence and acceptance of the suggested strategy in clinical settings would result from the development of techniques to clarify the reasoning behind CNN-based diagnoses [22].…”

Section: Methodsmentioning

confidence: 99%

“…The primary challenge that this research attempts to solve is how inadequate the present approaches are for identifying infections caused by STH, especially in areas with poor resources and restricted access to Int J Adv Appl Sci ISSN: 2252-8814  Enrichment of microscopic photographs by utilizing CNN regarding soil-transmitted … (Rio Andika Malik) 47 medical care. Even though STH infections are quite common and have a big effect on public health, the methods of identification that are now in use have a lot of drawbacks [22]. These include laborious procedures, a high risk of human error, and inefficiency.…”

Section: Introductionmentioning

confidence: 99%

“…The need for skilled personnel increases the likelihood of human errors, underscoring the urgency for a faster and more automated approach. Therefore, a faster and more automated approach is needed for detecting STH infections in microscopic images [22].…”

Section: Introductionmentioning

confidence: 99%

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Enrichment of microscopic photographs by utilizing CNN regarding soil-transmitted helminths identification

Malik,

Frimadani,

Putra

2024

IJAAS

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Soil-transmitted helminth (STH) infection remains a significant global health challenge, affecting millions of people, particularly in developing countries. A convolutional neural network (CNN) approach to optimize the detection of STH infections in microscopic images. The study aims to assess the effectiveness of the CNN model in identifying and classifying STH worm eggs accurately. The research employs MATLAB as the primary tool for conducting experiments and validation tests. By implementing image preprocessing techniques to enhance image quality and applying precise segmentation methods, the CNN model is trained on a dataset of microscopic images to learn and classify STH infections effectively. The validation test results demonstrate that the CNN model achieved a high accuracy rate of 92.31% in classifying STH infections. This accuracy surpasses traditional methods, which are time-consuming and susceptible to human errors. This study underscores the importance of integrating artificial intelligence, particularly CNN, into the healthcare domain to support detecting and diagnosing diseases requiring specialized expertise, such as STH infections. The findings of this research can serve as a valuable reference for researchers, medical practitioners, and data scientists in leveraging artificial intelligence to enhance the quality of healthcare services, leading to positive impacts on society worldwide.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations