Deep Learning-Based Automated Skin Cancer Detection Using Convolutional Neural Networks

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.

Skin cancer is one of the most dangerous types of cancer among the cancers. The early detection of skin cancer helps resolve it. Hence, it is necessary to diagnose the disease as early as possible. This paper presents Convolutional Neural Networks and the Vgg16 algorithm to recognize skin cancer types. The HAM10000 dataset, which comprises seven distinct forms of skin cancer, melanocytic nevi (nv), Melanoma (mel), basal cell carcinoma (bcc), actinic keratoses (akiec), vascular lesions (vasc), and dermatofibroma (df). This system aims to improve classification accuracy; the methodology necessitates extensive dataset preparation, including scaling, normalization, and augmentation. The Vgg16 algorithm, when combined with the CNN architecture, offers a robust basis for the classification of skin cancer. Comprehensive details on regularization techniques, optimization strategies, and training parameters are included to ensure openness and reproducibility. The system's performance is evaluated using accuracy, precision, recall, and F1-score for every type of skin cancer. This paper highlights the usefulness of the proposed method in skin cancer diagnosis and looks at challenges, constraints, and prospects for further research. New methods for identifying skin cancer are being developed with the help of this research, which can improve patient outcomes and clinical decision-making.

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 represents a significant global health issue, requiring precise and prompt identification to improve patient results. This study seeks to improve the precision of Convolutional Neural Networks (CNNs) for identifying skin cancer. Nonetheless, finding appropriate parameters for CNN poses a challenge for researchers. Optimizing hyperparameters has emerged as a crucial method for improving CNN performance. This research employs the Grey Wolf Optimizer (GWO) technique, a metaheuristic approach to optimize two essential hyperparameters: dropout rate and learning rate for CNN. The HAM10000 dataset, obtained from Kaggle and consisting of more than $\mathbf{1 0, 0 0 0}$ dermatoscopic images classified into seven skin lesion categories Actinic keratoses, Basal cell carcinoma, Benign keratosis-like lesions, Dermatofibroma, Melanocytic nevi, Melanoma, and Vascular lesions, was utilized for training and evaluation in this research. This research employs three unique data augmentation methods: rotation, flipping, and noise addition, alongside the combination of GWO with CNN, resulting in an increase in CNN accuracy to $87 \%$, exceeding earlier benchmarks and F1-scores for all lesion categories. These results emphasize the capability of GWO to greatly enhance the persuasiveness and flexibility of AI-based skin cancer detection models.

Dermatological problems are the most widely spread skin diseases amongst human beings. They can be infectious, chronic, and sometimes may also lead to serious health problems such as skin cancer. Generally, rural area clinics lack trained dermatologists and mostly rely on the analysis of remotely accessible experts through mobile-based networks for sharing the images and other related information. Under such circumstances, poor image quality introduced due to the capturing device results in misleading diagnosis. Here, a genetic-algorithm- (GA-) based approach used as an image enhancement technique has been explored to improve the low quality of the dermatological images received from the rural clinic. The diagnosis is performed on the enhanced images using convolutional neural network (CNN) classifier for the identification of the diseases. The scope of this paper is limited to only motion blurred images, which is the most prevalent problem in capturing of the images, specifically when any of the two (device or the object) may move unpredictably. Seven types of skin diseases, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, vascular lesion, and squamous cell carcinoma, have been investigated using ResNet-152 giving an overall accuracy of 87.40% for the blurred images. Use of GA-enhanced images increased the accuracy to 95.85%. The results were further analyzed using a confusion matrix and t-test-based statistical investigations. The advantage of the proposed technique is that it reduces the analysis time and errors due to manual diagnosis. Furthermore, speedy and reliable diagnosis at the earliest stage reduces the risk of developing more severe skin problems.

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.

There are several methods that have been discovered to improve the performance of Deep Learning (DL). Many of these methods reached the best performance of their models by tuning several parameters such as Transfer Learning, Data augmentation, Dropout, and Batch Normalization, while other selects the best optimizer and the best architecture for their model. This paper is mainly concerned with the optimization algorithms in DL. It proposes a modified version of Root Mean Squared Propagation (RMSProp) algorithm, called NRMSProp, to improve the speed of convergence, and to find the minimum of the loss function quicker than the original RMSProp optimizer. Moreover, NRMSProp takes the original algorithm, RMSProp, a step further by using the advantages of Nesterov Accelerated Gradient (NAG). It also takes in consideration the direction of the gradient at the next step, with respect to the history of the previous gradients, and adapts the value of the learning rate. As a result, this modification helps NRMSProp to convergence quicker than the original RMSProp, without any increase in the complexity of the RMSProp. In this work, many experiments had been conducted to evaluate the performance of NRMSProp with performing several tests with deep Convolution Neural Networks (CNNs) using different datasets on RMSProp, Adam, and NRMSProp optimizers. The experimental results showed that NRMSProp has achieved effective performance, and accuracy up to 0.97 in most cases, in comparison to RMSProp and Adam optimizers, without any increase in the complexity of the algorithm and with fine amount of memory and time.

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

Distribuição dos diagnósticos de lesões pré-neoplásicas e neoplásicas de pele no Hospital Universitário Evangélico de Curitiba

Concern about skin cancer is a common reason for people from predominantly fair-skinned populations to present to primary care doctors. To examine the frequency and body-site distribution of malignant, pre-malignant and benign pigmented skin lesions excised in primary care. This prospective study conducted in Queensland, Australia, included 154 primary care doctors. For all excised or biopsied lesions, doctors recorded the patient's age and sex, body site, level of patient pressure to excise, and the clinical diagnosis. Histological confirmation was obtained through pathology laboratories. Of 9650 skin lesions, 57·7% were excised in males and 75·0% excised in patients ≥ 50 years. The most common diagnoses were basal cell carcinoma (BCC) (35·1%) and squamous cell carcinoma (SCC) (19·7%). Compared with the whole body, the highest densities for SCC, BCC and actinic keratoses were observed on chronically sun-exposed areas of the body including the face in males and females, the scalp and ears in males, and the hands in females. The density of BCC was also high on intermittently or rarely exposed body sites. Females, younger patients and patients with melanocytic naevi were significantly more likely to exert moderate/high levels of pressure on the doctor to excise. More than half the excised lesions were skin cancer, which mostly occurred on the more chronically sun-exposed areas of the body. Information on the type and body-site distribution of skin lesions can aid in the diagnosis and planned management of skin cancer and other skin lesions commonly presented in primary care.

Skin Cancer is an extensive and possibly dangerous disorder that requires early detection for effective treatment. Add specific global statistics on skin cancer prevalence and mortality to emphasize the importance of early detection. Example: “Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. Manual skin cancer screening is both time-intensive and expensive. Deep learning (DL) techniques have shown exceptional performance in various applications and have been applied to systematize skin cancer diagnosis. However, training DL models for skin cancer diagnosis is challenging due to limited available data and the risk of overfitting. Traditionally approaches have High computational costs, a lack of interpretability, deal with numerous hyperparameters and spatial variation have always been problems with machine learning (ML) and DL. An innovative method called adaptive learning has been developed to overcome these problems. In this research, we advise an intelligent computer-aided system for automatic skin cancer diagnosis using a two-stage transfer learning approach and Pre-trained Convolutional Neural Networks (CNNs). CNNs are well-suited for learning hierarchical features from images. Annotated skin cancer photographs are utilized to detect ROIs and reset the initial layer of the pre-trained CNN. The lower-level layers learn about the characteristics and patterns of lesions and unaffected areas by fine-tuning the model. To capture high-level, global features specific to skin cancer, we replace the fully connected (FC) layers, responsible for encoding such features, with a new FC layer based on principal component analysis (PCA). This unsupervised technique enables the mining of discriminative features from the skin cancer images, effectively mitigating overfitting concerns and letting the model adjust structural features of skin cancer images, facilitating effective detection of skin cancer features. The system shows great potential in facilitating the initial screening of skin cancer patients, empowering healthcare professionals to make timely decisions regarding patient referrals to dermatologists or specialists for further diagnosis and appropriate treatment. Our advanced adaptive fine-tuned CNN approach for automatic skin cancer diagnosis offers a valuable tool for efficient and accurate early detection. By leveraging DL and transfer learning techniques, the system has the possible to transform skin cancer diagnosis and improve patient outcomes.

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

Acne and the malignant skin condition basal cell carcinoma along with other skin diseases dramatically affect global health. The promising capabilities of deep learning in dermatological classification exist only for isolated disease groups since studies exclusively analyze individual conditions without covering entire facial skin disorders. The study investigates CNN and Inception V3 models for the classification of five important conditions which include acne and actinic keratosis with basal cell carcinoma and eczema followed by rosacea. A total of 1,250 validated DermNet images were used for processing which included resizing along with normalization techniques and data enhancement methods. The Inception V3 model operates with implemented dense layers but a custom CNN model runs with an original structure of 5 convolutional and 2 dense layers. Both used Adam optimization and categorical cross-entropy loss. The test accuracy levels from Inception V3 (94%) outperformed those of CNN (93%) and the precision and recall coefficient reached 0.83 macro averages. The optimization process in CNN revealed overfitting since the training accuracy reached 88% while the test accuracy only reached 80%. Inception V3 outperformed other models by achieving F1 scores of 0.84 for rosacea and 0.86 for acne but CNN proved slightly better at identifying eczema with an F1 score of 0.81. The investigation proved that Inception V3 exhibits strong capabilities for diagnosing facial skin diseases through accurate scalable results. Despite dealing with restricted data the model kept outstanding performance levels through its implementation of transfer learning skills. Future research must increase the dataset size while conducting clinical tests to enhance universal usage. This research enables automated dermatological diagnosis to specifically identify five conditions which do not have current complete deep learning-based solutions.

Skin cancer is a kind of cancer that is usually diagnosed by images from dermoscopy. In recent years, researchers have attempted to utilize deep learning technology, especially convolution neural networks (CNN), in the recognition of skin cancer images. Many CNN models have already performed great applicability, like DenseNet, Inception, and Xception. This paper carried a comparative experiment on ISIC 2019 challenge dataset which includes 25,331 skin cancer images of 8 different kinds. On the classification task on the ISIC 2019, it introduced 6 models, VGGNet19, ResNet50, ResNet152, DenseNet201, Inception-v3, and Xception, then conducted a comparative analysis of their performance involving 2 methods (data enhance and transfer learning) and 2 optimizers (Adam and SGD), aiming to explore the impact of different methods and structures on the accuracy, in order to find traits for potential models of higher accuracy. In the 24 groups of results, Xception with data enhance, transfer learning (pretraining) and Adam optimizer had the highest accuracy of 83.8%, while VGGNet19 without transfer learning had the lowest of 66.67%. The influence of transfer leaning is positive on all models, both on accuracy and training time; similar to Adam optimizer, except for a noticeable enhancement effect on Inception-v3 and Xception. The data enhance method applied in this paper had a weak, non-directed impact. Possible reasons for this phenomenon are discussed in depth in the study.

- Skin cancer has become one of the most rapidly increasing and life-threatening diseases worldwide, appearing mainly as melanoma, basal cell carcinoma, and squamous cell carcinoma. Among these, melanoma is the most aggressive due to its high tendency to spread if not detected early. Early and accurate diagnosis greatly improves treatment outcomes, yet manual diagnosis is time-consuming and prone to human error. To overcome these challenges, this paper presents a computer-aided system for early skin cancer detection using image processing and deep learning techniques. The proposed system processes skin lesion images through pre-processing, segmentation, and classification using Convolutional Neural Networks (CNN). Besides cancer detection, such methods can also assist in identifying other dermatological conditions like eczema, psoriasis, and acne. A chatbot is integrated to provide users with instant support, symptom information, and guidance, making the platform efficient, interactive, and accessible for early diagnosis and patient awareness. . · Key Words: Image Processing, CNN, Deep Learning, Skin Cancer, Melanoma, Basal Cell Carcinoma, Squamous Cell Carcinoma, Chatbot, Skin Diseases