Automated Skin Cancer Detection: Where We Are and The Way to The Future

Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists. Skin disease is the most common disorder triggered by fungus, viruses, bacteria, allergies, etc. Skin diseases are most dangerous and may be the cause of serious damage. Therefore, it requires to diagnose it at an earlier stage, but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy. This advance therapy involves financial burden and some other ill effects. Therefore, it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage. Several techniques have been proposed to detect skin disease at an earlier stage but fail to get accuracy. Therefore, the primary goal of this paper is to classify, detect and provide accurate information about skin diseases. This paper deals with the same issue by proposing a high-performance Convolution neural network (CNN) to classify and detect skin disease at an earlier stage. The complete methodology is explained in different folds: firstly, the skin diseases images are preprocessed with processing techniques, and secondly, the important feature of the skin images are extracted. Thirdly, the pre-processed images are analyzed at different stages using a Deep Convolution Neural Network (DCNN). The approach proposed in this paper is simple, fast, and shows accurate results up to 98% and used to detect six different disease types.

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“…Neural Network with Optimization 99% [50] -- [51] histogram-based statistical method 77%, 67%, 92% [52] Capsule neural network -…”

Section: Related Workmentioning

confidence: 99%

Early Skin Disease Identification Using eep Neural Network

Gautam¹,

Trivedi²,

Anand³

et al. 2023

Computer Systems Science and Engineering

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“…[2][3][4][5] The acquisition of digital images of skin lesions further allows for the application of computer-assisted methods, which have shown to obtain more accurate diagnostic results. [6][7][8][9] However, the quality and appearance of digital skin lesion images depend on numerous factors. In fact, illumination conditions, device calibration, and operator dexterity are the main factors affecting the quality of digital dermatoscopic, and in general skin lesion, images.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, numerous studies in literature have also recently focused on the use of a smartphone camera for skin lesion image acquisition, which could provide a potential tool for general practitioners who can filter out the clearly benign skin lesions and consult expert dermatologists only in dubious cases 2–5 . The acquisition of digital images of skin lesions further allows for the application of computer‐assisted methods, which have shown to obtain more accurate diagnostic results 6–9 . However, the quality and appearance of digital skin lesion images depend on numerous factors.…”

Section: Introductionmentioning

confidence: 99%

Impact of artificial intelligence‐based color constancy on dermoscopical assessment of skin lesions: A comparative study

Branciforti,

Meiburger,

Zavattaro

et al. 2023

Skin Research and Technology

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BackgroundThe quality of dermoscopic images is affected by lighting conditions, operator experience, and device calibration. Color constancy algorithms reduce this variability by making images appear as if they were acquired under the same conditions, allowing artificial intelligence (AI)‐based methods to achieve better results. The impact of color constancy algorithms has not yet been evaluated from a clinical dermatologist's workflow point of view. Here we propose an in‐depth investigation of the impact of an AI‐based color constancy algorithm, called DermoCC‐GAN, on the skin lesion diagnostic routine.MethodsThree dermatologists, with different experience levels, carried out two assignments. The clinical experts evaluated key parameters such as perceived image quality, lesion diagnosis, and diagnosis confidence.ResultsWhen the DermoCC‐GAN color constancy algorithm was applied, the dermoscopic images were perceived to be of better quality overall. An increase in classification performance was observed, reaching a maximum accuracy of 74.67% for a six‐class classification task. Finally, the use of normalized images results in an increase in the level of self‐confidence in the qualitative diagnostic routine.ConclusionsFrom the conducted analysis, it is evident that the impact of AI‐based color constancy algorithms, such as DermoCC‐GAN, is positive and brings qualitative benefits to the clinical practitioner.

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“…Recent surveys are almost all focused on the adoption of artificial-intelligence techniques for the early detection of skin cancer. They observed the increasing interest of researchers for deep-learning techniques [ 20 , 21 ]. A key point that emerges from this analysis is the number of studies focusing on the automatic detection of lesions [ 22 ] or cancer.…”

Section: Introductionmentioning

confidence: 99%

A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

et al. 2023

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Skin detection involves identifying skin and non-skin areas in a digital image and is commonly used in various applications, such as analyzing hand gestures, tracking body parts, and facial recognition. The process of distinguishing between skin and non-skin regions in a digital image is widely used in a variety of applications, ranging from hand-gesture analysis to body-part tracking to facial recognition. Skin detection is a challenging problem that has received a lot of attention from experts and proposals from the research community in the context of intelligent systems, but the lack of common benchmarks and unified testing protocols has hampered fairness among approaches. Comparisons are very difficult. Recently, the success of deep neural networks has had a major impact on the field of image segmentation detection, resulting in various successful models to date. In this work, we survey the most recent research in this field and propose fair comparisons between approaches, using several different datasets. The main contributions of this work are (i) a comprehensive review of the literature on approaches to skin-color detection and a comparison of approaches that may help researchers and practitioners choose the best method for their application; (ii) a comprehensive list of datasets that report ground truth for skin detection; and (iii) a testing protocol for evaluating and comparing different skin-detection approaches. Moreover, we propose an ensemble of convolutional neural networks and transformers that obtains a state-of-the-art performance.

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Automated Skin Cancer Detection: Where We Are and The Way to The Future

Cited by 20 publications

References 21 publications

Early Skin Disease Identification Using eep Neural Network

Early Skin Disease Identification Using eep Neural Network

Impact of artificial intelligence‐based color constancy on dermoscopical assessment of skin lesions: A comparative study

A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

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