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Malignant Melanoma Types. (a) Superficial Spreading Melanoma. (b) Nodular Melanoma (c) Lentigo malignant Melanoma. (d) Acral Lentiginous Melanoma.
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Malignant melanoma is a kind of skin cancer that begins in melanocytes. It can influence on the skin only, or it may expand to the bones and organs. It is less common, but more serious and aggressive than other types of skin cancer. Malignant Melanoma can happen anywhere on the skin, but it is widespread in certain locations such as the legs in wom...
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Melanoma, a malignant form of skin cancer is very threatening to life. Diagnosis of melanoma at an earlier stage is highly needed as it has a very high cure rate. Benign and malignant forms of skin cancer can be detected by analyzing the lesions present on the surface of the skin using dermoscopic images. In this work, an automated skin lesion dete...
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... It provides a boost to the sensitivity of the information in images and presents enhanced input to carry out other procedures. In skin cancer recognition the research utilized an anisotropic diffusion filter [34,35], median filter [35,36], Dull Razor [37][38][39][40], Adam Huang algorithm [41], Wiener filter [42], Bilateral filter [43] and Gaussian and Gaussian Blur filter [44,45], Z-score transformation [46], contrast-limited adaptive histogram equalization [47,48], adaptive histogram equalization [49], global-local contrast stretching [50], color constancy with shades of gray [51], adaptive gamma correction [52], gamma and correction [53], etc., for enhancement and noise removal in the skin recognition process. ...
... It provides a boost to the sensitivity of the information in images and presents enhanced input to carry out other procedures. In skin cancer recognition the research utilized an anisotropic diffusion filter [34,35], median filter [35,36], Dull Razor [37][38][39][40], Adam Huang algorithm [41], Wiener filter [42], Bilateral filter [43] and Gaussian and Gaussian Blur filter [44,45], Z-score transformation [46], contrast-limited adaptive histogram equalization [47,48], adaptive histogram equalization [49], global-local contrast stretching [50], color constancy with shades of gray [51], adaptive gamma correction [52], gamma and correction [53], etc., for enhancement and noise removal in the skin recognition process. ...
The skin is the human body’s largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.
... It aims to enable computer systems to be improved automatically through experience by incorporating a variety of learning methods [27]. With the rapid development of various artificial intelligence technologies, machine learning methods have been widely used [1,24,25]. As a powerful neural network method, extreme learning machine (ELM) has become popular in the field of machine learning in the past decade [17][18][19]. ...
Recently, extreme learning machine (ELM) has attracted a lot of attention due to its high performance and extreme speed. However, how to improve the robustness of ELM has always been a problem. Considering the problems of noise and outliers in the experimental data, in this paper, we introduce the kernel risk-sensitive mean p-power error (KRP) into ELM and propose a robust ELM method named kernel risk-sensitive mean p-power error based robust extreme learning machine (KRPELM), on the basis of the high efficiency and robustness of KRP. In KRPELM, KRP function instead of square loss is integrated into ELM as the loss function, which can improve the robustness of ELM to noise and outliers. We also propose an efficient iterative adjustment strategy to optimize KRPELM. Nine benchmark datasets are utilized to verify the classification performance of the proposed KRPELM. In addition, we apply the proposed method to the classification of cancer samples. The experimental results on five cancer gene expression datasets show that KRPELM can identify different cancer types more accurately.
... This process is commonly known as metastasis. Malignant melanoma has four major subtypes: superficial spreading melanoma, nodular melanoma, lentigo malignant melanoma, and acral lentiginous melanoma [2]. The acral lentiginous melanoma is most commonly found in people with darker skin, such as Hispanic, African, and Asian ancestries. ...
Human skin is the most exposed part of the human body that needs constant protection and care from heat, light, dust, and direct exposure to other harmful radiation, such as UV rays. Skin cancer is one of the dangerous diseases found in humans. Melanoma is a form of skin cancer that begins in the cells (melanocytes) that control the pigment in human skin. Early detection and diagnosis of skin cancer, such as melanoma, is necessary to reduce the death rate due to skin cancer. In this paper, the classification of acral lentiginous melanoma, a type of melanoma with benign nevi, is being carried out. The proposed stacked ensemble method for melanoma classification uses different pre-trained models, such as Xception, Inceptionv3, InceptionResNet-V2, DenseNet121, and DenseNet201, by employing the concept of transfer learning and fine-tuning. The selection of pre-trained CNN architectures for transfer learning is based on models having the highest top-1 and top-5 accuracies on ImageNet. A novel stacked ensemble-based framework is presented to improve the generalizability and increase robustness by fusing fine-tuned pre-trained CNN models for acral lentiginous melanoma classification. The performance of the proposed method is evaluated by experimenting on a Figshare benchmark dataset. The impact of applying different augmentation techniques has also been analyzed through extensive experimentations. The results confirm that the proposed method outperforms state-of-the-art techniques and achieves an accuracy of 97.93%.
... With the rapid development in the field of artificial intelligence, there have been lots of practical applications using machine learning approaches and these applications are proved to be well-performed [1][2][3]. At the same time, many researchers continue making an intensive study in new and effective algorithms. ...
More recently, extreme learning machine (ELM) has emerged as a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. However, the single hidden layer NN using ELM may be not effective in addressing some large-scale problems with more computational efforts. To avoid such limitation, we utilize the multilayer ELM architecture in this article to reduce the computational complexity, without the physical memory limitation. Meanwhile, it is known to us all that there are a lot of noises in the practical applications, and the traditional ELM may not perform well in this instance. Considering the existence of noises or outliers in training dataset, we develop a more practical approach by incorporating the kernel risk-sensitive loss (KRSL) criterion into ELM, on the basis of the efficient performance surface of KRSL with high accuracy while still maintaining the robustness to outliers. A robust multilayer ELM, i.e., the stacked ELM using the minimum KRSL criterion (SELM-MKRSL), is accordingly proposed in this article to enhance the outlier robustness on large-scale and complicated dataset. The simulation results on some synthetic datasets indicate that the proposed approach SELM-MKRSL can achieve higher classification accuracy and is more robust to the noises compared with other state-of-the-art algorithms related to multilayer ELM.
... Skin cancer is currently one of the fastestgrowing tumors worldwide, and melanoma is the most life-threatening form of skin cancer [1][2]. Computer-aided diagnostics of skin disease is one of the more problematic difficulties in medical image processing [3][4][5]. ...
Automatic skin lesion segmentation on skin images is an essential
component in diagnosing skin cancer. Image de-noising in skin cancer lesion is a
description of processing image which refers to image restoration techniques to
develop an image in predefined touch. Then de-noising is the crucial step of
image processing to restore the right quality image after that which can use in
many processes like segmentation, detection. This work proposes a new
technique for skin lesion tumor denoising and segmentation. Initially, using Deep
Convolution Neural Network (CNN) to eliminate noise and undesired structures
for the images. Then, a new mechanism is proposed to segment the skin lesion
into skin images based on active_contour straight with morphological processes.
Different noise removal and segmentation techniques on skin lesion images are
applying and comparing. The proposed algorithm shows improvement in the
results of both noise reduction and segmentation.
... Melanoma is one of the most malignant tumours of the body. In this paper [8] related to this aggressive tumour, the authors focus again in early detection to allow a curative treatment, throughout digital imaging processing techniques. The other paper [10] is dedicated to Hematology and it analyzes the possibility of using image segmentation in the diagnosis of leukemia, a frequent blood cancer. ...
Although Medicine has always been considered a Health Science, today it is not possible to obviate its relationship with other disciplines as Humanities and Basic Sciences. Doctors from everywhere and everyday work with the most sophisticated technology are trying to make their profession more accurate and precise, taking in consideration, at the same time, the human part of their daily labour. In this volume of the Journal, we will try to explore the relation between different medical specialities, basic science and engineering. In fact, modern Medicine requires the participation of these professionals who are involved with doctors in multidisciplinary teams. In this sense, Medical Engineering, is a new degree that is offered in a vast number of Universities along the world. This relationship between Medicine and Sciences can be found in any medical speciality so that, our aim in this volume, is to show different examples of doctors working together with other scientifics in any area of Medical sciences.The volume consists on twelve papers. Each paper explores a particular area of this multidisciplinary approach.
Skin cancer has shown a rapidly growing event rate, highlighting skin cancer as a significant issue for general well-being. Impediment due to artifacts in dermoscopic images impacts the analytic activity and decreases the precision level. In this work, the De Trop Noise Exclusion - Inpainting (DNE-INPAINT) approach is proposed for the errand of artifacts evacuation and posterior mending on dermoscopic images. The methodology eliminates forefront white matter artifacts and reconstruction of missed pixels by a sequence of vintage boosting, Gray contrast stretching, filtering, mask creation for the noise existence areas and inpaint the masked areas relative with a source image. The experiments use the ISIC 2019 and ISIC 2018 dataset to prove the exhibition of the proposed technique that is demonstrated to be superior to other methods. To survey the quality of the outcomes acquired by the proposed methodology, concerning the original images, various metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and some other parameters, which are deliberated in result section.
