Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification

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Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Skin is one of the most significant organs, which serves as a barrier to the outside surroundings of the human body. To improve mortality, skin disease detection at a prior stage is necessary else it may convert to skin cancer. But its diagnosis at the prior stage that may increase life expectancy is a great experiment since it has a similar look to skin diseases. To deal with biomedical images, a new innovative automated system is required that can quickly and precisely identify skin lesions. Deep Learning is attracting a lot of attention in the treatment of numerous disorders. A fusion model is proposed here with the integration of the U-Net and Convolution Neural Network model. For this, the U-Net model has been utilized to segment the diseases using skin images and the Convolution Neural Network model has been proposed for the multi-class classification of segmented images. The model is simulated and analyzed using the HAM10000 dataset having 10,015 dermoscopy images of seven different classifications of skin diseases. The proposed model has been analyzed using two optimizers i.e. Adam and Adadelta on 20 epochs and 32 batch sizes for the skin disease classification. The model has outperformed on Adadelta optimizer with an accuracy value of 97.96%. 

Fusion of U-Net and CNN model for segmentation and classification of skin lesion from dermoscopy images

Although many efforts have been made through past years, skin cancer recognition from medical images is still an active area of research aiming at more accurate results. Many efforts have been made in recent years based on deep learning neural networks. Only a few, however, are based on a single deep learning model and targeted to create a mobile application. Contributing to both efforts, first we present a summary of the required medical knowledge on skin cancer, followed by an extensive summary of the most recent related works. Afterwards, we present 11 CNN (convolutional neural network) candidate single architectures. We train and test those 11 CNN architectures, using the HAM10000 dataset, concerning seven skin lesion classes. To face the imbalance problem and the high similarity between images of some skin lesions, we apply data augmentation (during training), transfer learning and fine-tuning. From the 11 CNN architecture configurations, DenseNet169 produced the best results. It achieved an accuracy of 92.25%, a recall (sensitivity) of 93.59% and an F1-score of 93.27%, which outperforms existing state-of-the-art efforts. We used a light version of DenseNet169 in constructing a mobile android application, which was mapped as a two-class model (benign or malignant). A picture is taken via the mobile device camera, and after manual cropping, it is classified into benign or malignant type. The application can also inform the user about the allowed sun exposition time based on the current UV radiation degree, the phototype of the user’s skin and the degree of the used sunscreen. In conclusion, we achieved state-of-the-art results in skin cancer recognition based on a single, relatively light deep learning model, which we also used in a mobile application.

https://www.mdpi.com/2079-9292/11/9/1294/pdf?version=1650358696

Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application

An automated deep learning models for classification of skin disease using Dermoscopy images: a comprehensive study

Pigmented skin lesions are common spots or growths on the skin that originate from melanocyte cells. Skin cancer occurs as a result of the uncontrolled division of melanocyte cells. Skin cancer is common worldwide and its incidence has been increasing. Timely and accurate diagnosis of skin cancer is important in reducing deaths. Skin cancer cases are diagnosed by expert dermatologists, but the number of experts is insufficient for the population. Dermoscopy is one of the most commonly used methods for imaging skin lesions. Sometimes misdiagnosis can occur in the interpretation of dermoscopic images by human experts. Computer-aided systems help to make accurate and objective decisions in the diagnosis of skin lesions. Convolutional neural network(CNN) is a deep learning technique commonly used in the field of computer vision and is also widely used in medical image analysis In this study, a CNN model was proposed to classify seven different skin lesions in the HAM10000 dataset. The model provided a classification accuracy of 91.51%. The performance of the model was compared with similar studies in the literature and it was found that it showed higher success than most studies. The model was connected to a web application and evaluated in two phases by seven expert dermatologists. In the first phase, it was concluded that the model could diagnose skin lesions with 90.28% accuracy in practice. In the second phase, the model corrected the experts' misdiagnoses by 11.14%.

A deep convolutional neural network-based pigmented skin lesion classification application and experts evaluation

Skin cancer is one of the major types of cancers that can arise from various dermatological disorders and can be classified into various types according to texture, structure, color and other morphological features. Identifying the lesions from skin images can be an important step in pre-diagnosis to aid the doctors and infer the medical condition of the patient. Recent work has focused on classifying only melanoma from a given set of skin lesion images. However, some types of skin lesions (Acctinic Keratosis and basal cell carcinoma) can become malignant over a period of time. So by detecting these classes we can say we are cutting down the risk of malignancy and doing the task of early detection. We are able to classify different types of skin lesions (basal cell carcinoma, benign keratosis, dermatofibroma, vascular lesions, melanoma, and melanocytic nevi) with an accuracy of above 80% with Mobilenet, VGG-16 and our custom model which we have designed. With the help of this models, which will be embedded in skin lesion analyzer machines. This can give the patients as well as doctors a good idea of whether or not there is a need for medical attention and can avoid unnecessary panic/false alarms. We are using different deep learning architectures to classify skin lesions with good accuracy relative to existing work.

Shalaka Vaze

Papers

Skin Lesion Classification using Deep Learning Architectures