Skin cancer and deep learning for dermoscopic images classification: A pilot study.

Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, deep learning algorithms have been proposed. Deep learning algorithms have shown diagnostic efficacy comparable to dermatologists in the discipline of images-based skin lesion diagnosis in recent research articles. This work proposes a novel deep learning algorithm to detect skin cancer. The proposed CAD-Skin system detects and classifies skin lesions using deep convolutional neural networks and autoencoders to improve the classification efficiency of skin cancer. The CAD-Skin system was designed and developed by the use of the modern preprocessing approach, which is a combination of multi-scale retinex, gamma correction, unsharp masking, and contrast-limited adaptive histogram equalization. In this work, we have implemented a data augmentation strategy to deal with unbalanced datasets. This step improves the model's resilience to different pigmented skin conditions and avoids overfitting. Additionally, a Quantum Support Vector Machine (QSVM) algorithm is integrated for final-stage classification. Our proposed CAD-Skin enhances category recognition for different skin disease severities, including actinic keratosis, malignant melanoma, and other skin cancers. The proposed system was tested using the PAD-UFES-20-Modified, ISIC-2018, and ISIC-2019 datasets. The system reached accuracy rates of 98%, 99%, and 99%, consecutively, which is higher than state-of-the-art work in the literature. The minimum accuracy achieved for certain skin disorder diseases reached 97.43%. Our research study demonstrates that the proposed CAD-Skin provides precise diagnosis and timely detection of skin abnormalities, diversifying options for doctors and enhancing patient satisfaction during medical practice.

According to Skin Cancer Foundation, skin cancer is by far the most common type of cancer in the United States and worldwide. Early diagnosis of skin cancer is critical because proper treatment at early stages can increase the chance of cure and recovery. However, visual inspection of dermoscopic images by dermatologists is error-prone and time-consuming. To ensure accurate diagnosis and faster treatment of skin cancer, deep learning techniques have been utilized to conduct automated skin lesion segmentation and classification. In this paper, after image processing, a Mask R-CNN model is built for lesion segmentation, where transfer learning is utilized by using the pre-trained weights from Microsoft COCO dataset. The weights of the trained Mask R-CNN model are saved and transferred to the next task - skin lesion classification, to train a Mask R-CNN model for classification. Our experiments are conducted on the benchmark datasets from the International Skin Imaging Collaboration 2018 (ISIC 2018) and evaluated by the same metrics used in ISIC 2018. The lesion boundary segmentation and lesion classification have achieved an accuracy of 96% and a balanced multiclass accuracy of 80%, respectively.

Skin cancer is one of the most common cancers, and its early detection can have a huge impact on its outcomes. Deep learning, especially convolutional neural networks, perform well in processing massive amounts of data, especially image data in classifying skin cancer. In this paper, convolutional neural networks are mainly used to diagnose and classify 7 types of skin lesions, including melanoma, basal cell carcinoma, melanocytic nevus, actinic keratosis, and intraepithelial carcinoma, benign keratinoid lesions, dermatofibroma, and vascular lesions. First, the characteristics of skin lesion images are analyzed, using image processing technology and sampling technology to preprocess skin lesion images. Then the training parameters of imageNet network are adjusted through the idea of transfer learning on InceptionV3, ResNet50, DenseNet201, and other networks to perform training classification. Furthermore, different convolutional neural network models are built for classification. In order to validate the classification performance of various convolutional neural network models, this paper adopts ISIC 2017 HAM10000 dataset for experiments. The experimental results show that proper preprocessing is necessary when applying CNN for image classification. In classifying the 224*224 skin lesion images, the classical deep convolutional network with DenseNet201 achieved a remarkable performance classification accuracy of 99.12% for training and 86.91% for testing.

The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin cancer is considered to be the deadliest and most severe kind of cancer. Medical professionals utilize dermoscopy images to make a manual diagnosis of skin cancer. This method is labor-intensive and time-consuming and demands a considerable level of expertise. Automated detection methods are necessary for the early detection of skin cancer. The occurrence of hair and air bubbles in dermoscopic images affects the diagnosis of skin cancer. This research aims to classify eight different types of skin cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous cell carcinoma (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), and benign keratosis (BKs). In this study, we propose SNC_Net, which integrates features derived from dermoscopic images through deep learning (DL) models and handcrafted (HC) feature extraction methods with the aim of improving the performance of the classifier. A convolutional neural network (CNN) is employed for classification. Dermoscopy images from the publicly accessible ISIC 2019 dataset for skin cancer detection is utilized to train and validate the model. The performance of the proposed model is compared with four baseline models, namely EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), and ResNet-101 (B4), and six state-of-the-art (SOTA) classifiers. With an accuracy of 97.81%, a precision of 98.31%, a recall of 97.89%, and an F1 score of 98.10%, the proposed model outperformed the SOTA classifiers as well as the four baseline models. Moreover, an Ablation study is also performed on the proposed method to validate its performance. The proposed method therefore assists dermatologists and other medical professionals in early skin cancer detection.

Skin cancer is the most commonly diagnosed cancer worldwide. It is estimated that there are over 5 million cases of skin cancer are diagnosed in the United States every year. Although less than 5% of all diagnosed skin cancers are melanoma it accounts for over 70% of skin cancer-related deaths. In the past decade, the number of melanoma cancer cases has increased by 53%. Recently, there has been significant work on segmentation and classification of skin lesions via deep learning. However, there is limited work on identifying attributes and clinically-meaningful visual skin lesion patterns from dermoscopic images. In this work, we propose to use conditional GANs for skin lesion segmentation and attribute detection and use these attributes to improve skin lesion classification. The proposed conditional GAN framework can generate segmentation and attribute masks from RGB dermoscopic images. The adversarial-image-to-image translation style architecture forces the generator to learn both local and global features. The Markovian discriminator classifies pairs of image and segmentation labels as being real or fake. Unlike previous approaches, such an architecture not only learns the mapping from dermoscopic images image to segmentation and attribute masks but also learns an optimal loss function to train such a mapping. We demonstrate that the such an approach significantly improves the Jaccard index for segmentation (with a 0.65 threshold) up to 0.893. Fusing the lesion attributes for classification of lesions yields a higher accuracy compared to those without predicted attributes.

Skin cancers are one of the most common cancers in the world. Early detections and treatments of skin cancers can greatly improve the survival rates of patients. In this paper, a skin lesions classification system is developed with deep convolutional neural networks of ResNet50, which may help dermatologists to recognize skin cancers earlier. We utilize the ResNet50 as a pre-trained model. Then, by transfer learning, it is trained on our skin lesions dataset. Image preprocessing and dataset balancing methods are used to increase the accuracy of the classification model. In classification of skin diseases, our model achieves an overall accuracy of 83.74% on nine-class skin lesions. The experimental results show an impressive effect of the ResNet50 model in finegrained skin lesions classification and skin cancers recognition.

Skin cancer is a growing global health concern, especially for people with long sun exposure or lighter skin. It develops when abnormal skin cells multiply uncontrollably and often goes unnoticed in the early stages. Detecting it early is vital to reduce deaths, particularly in areas with limited access to dermatologists. Deep learning models can quickly and accurately identify skin lesions from dermoscopic images without needing medical specialists. Often called a silent threat, skin cancer can mimic harmless moles or minor skin changes, making it easy to miss. Among image processing methods, YOLO offers fast real-time detection of suspicious lesions, while DenseNet121 improves classification with strong feature extraction. This study introduces a hybrid YOLOv8– DenseNet121 model trained on the HAM10000 dataset to identify and classify skin lesions, supporting early treatment and better outcomes in diverse healthcare settings.The proposed hybrid YOLOv8–DenseNet121 model got an overall accuracy of approximately 97% on the HAM10000 dataset, demonstrating its effectiveness in correctly identifying and classifying skin lesions.

Due to the rising occurrence of skin cancer and inadequate clinical expertise, it is needed to design Artificial Intelligence (AI) based tools to diagnose skin cancer at an earlier stage. Since massive skin lesion datasets have existed in the literature, the AI-based Deep Learning (DL) models find useful to differentiate benign and malignant skin lesions using dermoscopic images. This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet (ARGS-OEN) technique for skin lesion segmentation and classification. The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower Pollination Algorithm (FPA). In addition, Multiwheel Attention Memory Network Encoder (MWAMNE) based classification technique is employed for identifying the appropriate class labels of the dermoscopic images. A comprehensive simulation analysis of the ASRGS-OEN technique takes place and the results are inspected under several dimensions. The simulation results highlighted the supremacy of the ASRGS-OEN technique on the applied dermoscopic images compared to the recently developed approaches.

Nonmelanoma skin cancer (NMSC) is the most common type of cancer that affects the Caucasian population. Approximately 80% of NMSCs are basal cell carcinoma (BCC) and 20% are squamous cell carcinoma (SCC). Actinic keratosis (AK) is a precancerous lesion that may develop into SCC. A market research survey was conducted in which dermatologists and primary care physicians (PCPs) were randomly selected from seven countries (USA, Australia, UK, Italy, France, Germany and Spain). Their knowledge of nonmelanoma skin cancer and their current clinical practice were assessed. In total, 2100 physicians took part in the survey. They had practised medicine for between 1 and 30 years and saw at least 30 patients in a typical week. The majority of dermatologists (97%) were familiar with BCC and AK, and treated each condition with a minimum of referrals. PCPs were more familiar with BCC (90%) than with AK (74%). Of the PCPs that were aware of BCC, only 31% treated the condition, and of those aware of AK, 40% treated the condition. Surgery was the most common choice of treatment for BCC. The most popular treatment choice for AK lesions was cryotherapy. Eighty to 100% of physicians reported that they discussed skin cancer prevention with their patients. A much lower number of physicians (ranging from 5 to 37%) provided educational material to patients. Overall, PCPs in the two countries that have a high incidence of NMSC (USA and Australia) were more familiar with BCC and AK and more likely to treat each condition than PCPs in Europe. All physicians rated BCC as a more serious condition than AK. Facial lesions were considered more serious than lesions on the head or trunk for both conditions. As the burden of disease and the number of patients seeking treatment for NMSC increase, dermatologists are well placed to lead educational initiatives for PCPs and provide educational material for patients. This would increase awareness of AK and BCC and could improve early diagnosis.

Melanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesions, which lead to incorrect classification. Accurate classification of a skin lesion in its early stages saves human life. In this paper, a highly accurate method proposed for the skin lesion classification process. The proposed method utilized transfer learning with pre-trained AlexNet. The parameters of the original model used as initial values, where we randomly initialize the weights of the last three replaced layers. The proposed method was tested using the most recent public dataset, ISIC 2018. Based on the obtained results, we could say that the proposed method achieved a great success where it accurately classifies the skin lesions into seven classes. These classes are melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, and vascular lesion. The achieved percentages are 98.70%, 95.60%, 99.27%, and 95.06% for accuracy, sensitivity, specificity, and precision, respectively.

Not knowing the type of skin lesion or too much-delayed diagnosis can lead to chronic disease or skin cancer. In SkinAid, we propose an application that can not only assist the dermatologist in obtaining a preliminary analysis of detecting, classifying and monitoring the skin lesion but also creates awareness for the user to care for the skin. SkinAid classifies and provides information on several skin lesions. In this regard, the highly unbalanced and limited data leads the existing Deep Learning models to overfit or poorly generalize, resulting in re-duced performance. We explore Generative Adversarial Networks (GANs) to augment and enhance the dataset. Also, we trained a Deep Convolutional Neural Network (CNN) model for edge computing platforms that can automatically detect and classify skin lesions from the captured image of the user's skin. A single-board computer and a smartphone employed as edge platforms have been implemented in SkinAid. The SkinAid model detects and classifies 7 different skin lesions with an overall accuracy of 92.2%. A health worker or patient can capture the real-time skin lesion images using a smartphone app camera to obtain a preliminary analysis to diagnose the skin lesion in Internet-of-Medical-Things (IoMT) platform for remote monitoring by dermatologists.

Recently, deep neural networks achieved state-of-the-art results on the automated diagnosis of skin lesions. Both the availability of bigger and better datasets as well as major advancements in Convolutional Neural Network methodologies represent some of the reasons behind these results. While the former is powered by initiatives like the International Skin Imaging Collaboration (ISIC), the latter is potentiated by developments in CNN architectures and the rise of transfer learning. This paper addresses open research questions related to the effectiveness of transfer learning methods in the context of multi-class skin lesion classification. The results indicate that, depending on the way pre-trained models are re-purposed, recent CNN architectures can bring significant performance boosts on the overall performance of deep learning classifiers. Experiments also highlight the importance of a good dataset to train these models, and how class balancing through data augmentation can help ease this requirement. Furthermore, experimentation with different models shows that ensembles can bring an edge over single-model approaches. Finally, this work presents a competitive single- and multi-model approach to the ISIC 2019 challenge.

<span lang="EN-US">One type of skin cancer that is considered a malignant tumor is melanoma. Such a dangerous disease can cause a lot of death in the world. The early detection of skin lesions becomes an important task in the diagnosis of skin cancer. Recently, a machine learning paradigm emerged known as deep learning (DL) utilized for skin lesions classification. However, in some previous studies by using seven class images diagnostic of skin lesions classification based on a single DL approach with CNNs architecture does not produce a satisfying performance. The DL approach allows the development of a medical image analysis system for improving performance, such as the deep convolutional neural networks (DCNNs) method. In this study, we propose an ensemble learning approach that combines three DCNNs architectures such as Inception V3, Inception ResNet V2 and DenseNet 201 for improving the performance in terms of accuracy, sensitivity, specificity, precision, and F1-score. Seven classes of dermoscopy image categories of skin lesions are utilized with 10015 dermoscopy images from well-known the HAM10000 dataset. The proposed model produces good classification performance with 97.23% accuracy, 90.12% sensitivity, 97.73% specificity, 82.01% precision, and 85.01% F1-Score. This method gives promising results in classifying skin lesions for cancer diagnosis.</span>

200 Risks and timing of future skin cancers in patients with actinic keratosis

Non-melanoma skin cancer (NMSC) is generally considered to be a worldwide epidemic. Current estimates predict that between 0.9 and 1.2 million new cases will be diagnosed each year in the United States of America (USA), which is one-third of all cancers diagnosed (1). In Australia, the incidence of NMSC continues to rise and it now affects at least 1% to 2% of the population annually (1, 2). NMSC tends to affect Caucasians particularly in areas exposed to sunlight, such as the head, neck and back of the hands. There is an increased risk with fair skin, blue eyes and a history of repeated sunburns (1). There are two main forms of NMSC, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), which account for 80% and 16% respectively (1). The majority of BCCs present as two varieties. Nodular BCC (nBCC) appears mostly on the head and neck and accounts for approximately 60% of all BCCs (3). Most of the remainder are superficial BCC (sBCC), which occur on the trunk and extremities (4, 5). Standard treatments for BCCs include excision, curettage and electrodesiccation, and cryosurgery (6). Other forms of treatment such as Mohs micrographic surgery, intralesional interferon (7) and radiotherapy, may be used depending on the nature, site and size of the tumor (6). Actinic keratosis (AK) is considered by some to be the earliest clinically recognizable manifestation of SCC in situ (8, 9). The risk of progression of AK to invasive SCC has been reported as ranging from 0.25 to 20% within 10 – 25 years (8, 10, 11). Sun avoidance measures and sunscreen protection are the first steps in the therapy of AK. The conventional approaches for treating AKs include cryotherapy, electrodesiccation and curettage, excision, CO2 laser therapy, 5-fluorouracil (5-FU), chemical peels and photodynamic therapy (12). Recently, topical immunotherapy in the form of imiquimod 5% cream has demonstrated efficacy for the treatment of BCC and AK. Imiquimod, an immune response modifier, has demonstrated in pre-clinical studies to have both antiviral and antitumor activity in vivo (13). It is currently recommended for the treatment of external genital warts induced by human papillomavirus (14 – 16). This paper reviews the evidence of the safety and efficacy of imiquimod for the treatment of BCC and AK. IMIQUIMOD FOR THE TREATMENT OF BASAL CELL CARCINOMA