Skin Disease Recognition: A Machine Vision Based Approach

Nawar, Anika; Sabuz, Noor Kibria; Siddiquee, Shah Md. Tanvir; Rabbani, Masud; Biswas, Al Amin; Majumder, Anup

doi:10.1109/icaccs51430.2021.9441980

Cited by 13 publications

(2 citation statements)

References 6 publications

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“…Early detection of diseases is usually vital for effective and timely intervention [ 23 , 24 ]. With current medical devices and technology, early detection or identification is getting better, but people may not reach to diagnosis centers in time or are too expensive for some, especially in developing countries [ 25 ]. The symptoms and signs of some of the diseases are too similar and asymmetric for extremely large areas, making identification difficult [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Fungal Skin Disease Classification Using the Convolutional Neural Network

Nigat,

Sitote,

Gedefaw

2023

Journal of Healthcare Engineering

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Skin is the outer cover of our body, which protects vital organs from harm. This important body part is often affected by a series of infections caused by fungus, bacteria, viruses, allergies, and dust. Millions of people suffer from skin diseases. It is one of the common causes of infection in sub-Saharan Africa. Skin disease can also be the cause of stigma and discrimination. Early and accurate diagnosis of skin disease can be vital for effective treatment. Laser and photonics-based technologies are used for the diagnosis of skin disease. These technologies are expensive and not affordable, especially for resource-limited countries like Ethiopia. Hence, image-based methods can be effective in reducing cost and time. There are previous studies on image-based diagnosis for skin disease. However, there are few scientific studies on tinea pedis and tinea corporis. In this study, the convolution neural network (CNN) has been used to classify fungal skin disease. The classification was carried out on the four most common fungal skin diseases: tinea pedis, tinea capitis, tinea corporis, and tinea unguium. The dataset consisted of a total of 407 fungal skin lesions collected from Dr. Gerbi Medium Clinic, Jimma, Ethiopia. Normalization of image size, conversion of RGB to grayscale, and balancing the intensity of the image have been carried out. Images were normalized to three sizes: 120 × 120, 150 × 150, and 224 × 224. Then, augmentation was applied. The developed model classified the four common fungal skin diseases with 93.3% accuracy. Comparisons were made with similar CNN architectures: MobileNetV2 and ResNet 50, and the proposed model was superior to both. This study may be an important addition to the very limited work on the detection of fungal skin disease. It can be used to build an automated image-based screening system for dermatology at an initial stage.

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Section: Methodsmentioning

confidence: 99%

Fungal Skin Disease Classification Using the Convolutional Neural Network

Nigat,

Sitote,

Gedefaw

2023

Journal of Healthcare Engineering

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“…In general, a dermatologist may perform a variety of tests such as a physical examination of the suspicious area, followed by dermoscopy, and finally a blood test. [1][2][3][4][5] This will take time and patients may progress to later stages. Reports show that even the best dermatologists have a less than 85 percentage accuracy rate in diagnosing skin cancer.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Patient Care Mobile Application for Detecting Skin Cancer

M. Mercy Jemima,

Dr. J.K. Kanimozhi

2024

Int Res J Adv Engg Hub

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Over time, skin cancer has become one of the most lethal malignancies in humans. Melanoma is the most dangerous type of skin cancer and is difficult to anticipate. Early identification is critical for maximising patients' chances of survival and preventing cancer from spreading to other areas of the body. It is very important to found and treat melanoma, before it spreads to the lymph nodes. So, the early finding is very vital. Skin cancer is normally diagnosed visually; it begins with physical examination, followed by dermoscopy, biopsy and histopathology, which takes several days. The major issue with melanoma is that, the disease can pave to some other cells. Laboratory sampling may cause the increase of lesion. Therefore, there must be an application that can perform the fastest, most accurate and cheapest diagnose. Computer based diagnosis can get better skin cancer diagnosis and it will work effectively to the disease symptoms. Our solutions will create a mobile cloud computing application that will take photos of the affected tumor and returns showing whether it is malignant or benign. Our solution will build a mobile application on cloud computing and it takes the pictures of affected skin tumors and gives result as whether it is affected as either Malignant or Benign.

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