Skin Lesions Classification and Segmentation: A Review

Stofa, Marzuraikah Mohd; Zulkifley, Mohd Asyraf; Zainuri, Muhammad Ammirrul Atiqi Mohd

doi:10.14569/ijacsa.2021.0121060

Cited by 8 publications

(3 citation statements)

References 54 publications

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“…The evaluation and comparison of various glaucoma diagnosis techniques depend heavily on the choice of appropriate performance metrics. Table 3 lists the most commonly used metrics along with their descriptions [ 47 , 53 , 54 ].…”

Section: Feature Enhancement and Evaluationmentioning

confidence: 99%

Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review

et al. 2023

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Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner.

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Section: Feature Enhancement and Evaluationmentioning

confidence: 99%

Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review

et al. 2023

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“…The design and extraction of the hand-crafted features were the key concerns for creating such a system, and the deployment was severely constrained by the complexity of these procedures. Deep learning capabilities for image processing, especially for image segmentation, have been heavily used by medical researchers and include various modalities of 2D CNN, 2.5D CNN, and 3D CNN [ 104 , 105 ].…”

Section: Segmentation Processmentioning

confidence: 99%

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

et al. 2023

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One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient’s probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.

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“…Their ability to discern intricate patterns and subtle variations in skin images results in more reliable and precise assessments. In contrast, conventional diagnostic techniques heavily rely on the subjective interpretation and experience of dermatologists, which can introduce variability and human error(Han et al, 2018;Stofa et al, 2021).…”

mentioning

confidence: 99%

Exploring the Potential of Convolutional Neural Networks in Healthcare Engineering for Skin Disease Identification

Jilani Sayyad

2023

IJRITCC

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Skin disorders affect millions of individuals worldwide, underscoring the urgency of swift and accurate detection for optimal treatment outcomes. Convolutional Neural Networks (CNNs) have emerged as valuable assets for automating the identification of skin ailments. This paper conducts an exhaustive examination of the latest advancements in CNN-driven skin condition detection. Within dermatological applications, CNNs proficiently analyze intricate visual motifs and extricate distinctive features from skin imaging datasets. By undergoing training on extensive data repositories, CNNs proficiently classify an array of skin maladies such as melanoma, psoriasis, eczema, and acne. The paper spotlights pivotal progressions in CNN-centered skin ailment diagnosis, encompassing diverse CNN architectures, refinement methodologies, and data augmentation tactics. Moreover, the integration of transfer learning and ensemble approaches has further amplified the efficacy of CNN models. Despite their substantial potential, there exist pertinent challenges. The comprehensive portrayal of skin afflictions and the mitigation of biases mandate access to extensive and varied data pools. The quest for comprehending the decision-making processes propelling CNN models remains an ongoing endeavor. Ethical quandaries like algorithmic predisposition and data privacy also warrant significant consideration. By meticulously scrutinizing the evolutions, obstacles, and potential of CNN-oriented skin disorder diagnosis, this critique provides invaluable insights to researchers and medical professionals. It underscores the importance of precise and efficacious diagnostic instruments in ameliorating patient outcomes and curbing healthcare expenditures.

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Skin Lesions Classification and Segmentation: A Review

Cited by 8 publications

References 54 publications

Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review

Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

Exploring the Potential of Convolutional Neural Networks in Healthcare Engineering for Skin Disease Identification

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