A Comparative Study Among Segmentation Techniques for Skin Disease Detection Systems

Mamun, Md. Al; Uddin, Mohammad Shorif

doi:10.1007/978-981-33-4673-4_14

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…We explore the suggested methods in depth in this section. Image preprocessing is a critical stage in the detection method since it allows for noise removals such as hairs, clothes, and other artifacts while also improving the characteristics of the original image [ 16 ]. The primary goal of this phase is to improve the quality of the epidermal picture by eliminating irrelevant and redundant components from the image’s backgrounds for further treatment.…”

Section: System Modelmentioning

confidence: 99%

“…They can identify the condition using image processing methods such as picture modification, equalization, enrichment, feature extraction, and classification combined with traditional images. The skin images collected for illness classification and identification are handled and fed into advanced automation strategies such as ML, DL, ANN, CNN, and back propagation neural networks, as well as classification methods such as SVM and NB classification methods for the prognostication of skincare product illnesses [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Skin disorders may also be identified using image processing techniques such as mathematical morphology for texture analysis [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Approaches for Prognosis of Automated Skin Disease

Kshirsagar

Manoharan²,

Shitharth

et al. 2022

Life

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Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts.

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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches for Prognosis of Automated Skin Disease

Kshirsagar

Manoharan²,

Shitharth

et al. 2022

Life

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“…Predictability is also an Journal of Computer and Communications important criterion for an automated skin disease detection system. Machine learning and artificial intelligence algorithms are used to predict skin diseases [7]- [12]. The predicted skin diseases are classified as healthy, benign, suspicious, and malignant.…”

Section: Design Requirements For Hybrid Skin Disease Detection Systemmentioning

confidence: 99%

“…Finally, the testing and learning units identify the various skin diseases. If there exist no matches between the test and stored database images, the system will be rechecked [12]. If we can add a treatment plan, it would be very helpful for the patients [13].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Methodologies for Segmentation and Classification of Skin Diseases: A Study

Mamun¹,

Uddin²

2021

JCC

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Skin disorders are a serious global health problem for humans. These disorders become dangerous when they grow into the malignant stage. Hence, it is necessary to detect these diseases at their early stage. A mobile-based automated skin disease detection system is vital for detecting skin diseases. This system also offers cure or treatment plans to the affected person through the short message service (SMS) or electronic mail (e-mail). An effective skin disease detection system consists of three processes: segmentation, feature extraction, and classification. Several hybrid methodologies are already developed for the above-mentioned processes for detecting skin diseases at the initial stage. This research gives a standard hybrid framework for detecting skin diseases and highlights some design requirements for achieving high accuracy. Existing state-of-the-art hybrid methods of three processes for detecting skin diseases along with their limitations are also summarized. It also identifies the challenges for developing an effective skin disease detection system and gives future research directions.

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