Skin Cancer Detection and Intensity Calculator using Deep Learning

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.

Classification of skin cancer is an important task to detect skin cancer and help with the treatment of skin cancer according to its type. There are many techniques in imaging used to classify skin cancer, one of the superior deep learning (DL) algorithms for classification is the Convolutional Neural Network (CNN). One type of skin cancer is dangerous is melanoma. In this study, CNN is proposed to help classify this type of skin cancer. The dataset consists of 15103 images of skin cancer pigments with 7 different types of skin cancer. These three tests proved malignant skin lesions can be classified with higher accuracy than non-melanocytic skin lesions which is 90% and performance evaluation shows melanocytic and non-melanocytic skin lesions detected with the highest accuracy. The tests conducted in this study grouped several types of skin diseases namely the first tests conducted using a group of melanocytic and non-melanocytic skin disease, second testing using groups of melanoma and melanocytic nevus diseases, and the final testing using malignant and benign. The proposed CNN model achieved significant performance with a best accuracy of 94% on the classification of melanoma and melanocytic nevus.

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.

ABSTRACTSkin cancer is the most common cancer affecting the U.S. population. Pinterest.com, a virtual bookmarking social media site, has the potential to disseminate skin cancer-related information among young women, the group with the fastest increase in skin cancer diagnosis. This article presents a quantitative content analysis of pins about skin cancer on Pinterest guided by agenda-setting theory and the health belief model. Overall, sun exposure and tanning beds were most frequently discussed as the causes of skin cancer, and alternative therapies such as herbal medicine were discussed more than traditional biomedical treatment or prevention. Highly repinned pins tend to include more information than regular pins. Different types of skin cancer (melanoma, squamous-cell carcinoma, and basal-cell carcinoma) received the same amount of coverage; however, pins about nonmelanoma skin cancer (such as squamous-cell carcinoma and basal-cell carcinoma) were often information-poor. They were less likely to include information on the causes, prevention, and the biomedical treatment of skin cancer and were less likely to include health belief constructs associated with the promotion of skin cancer prevention and treatment.

Conflict of interest: the authors declare that they have no conflicts of interest. The recent publication by van der Geer et al.1 concludes that ‘the number of nonmelanoma skin cancers (NMSCs) differs substantially in practice from the number of first primary histologically confirmed NMSCs, as usually reported by population‐based cancer registries such as the Eindhoven Cancer Registry (ECR)'. We fully agree with this unsurprising conclusion, as designation of ‘first primary’ implies that there may be more tumours, and it is well known that a substantial proportion of patients with basal cell carcinoma (BCC) develop one or more subsequent BCCs, depending on age at first diagnosis and intensity of surveillance.2,3,4,5 However, we disagree with the methodology and with the negative interpretation of work by population‐based registries. The random sample of patients with skin cancer studied by van der Geer et al.1 was treated in a highly specialized dermatology clinic, as illustrated by the reported high proportion of patients treated with Mohs micrographic surgery. In such a population, the proportion of patients with high‐risk BCC who develop multiple primaries is most likely much higher than in the general BCC population. The authors failed to adequately specify the characteristics of the population, and patient selection was unclear, and included first and subsequent skin cancers. In addition, the study combined all skin cancers for the estimation of the percentage of patients with multiple skin cancers over time, whereas risk of subsequent malignancies differs substantially for the different types of skin cancer.4

Though the immune system plays a crucial role in skin cancer development, associations between immune-related diseases and skin cancer have not been well studied in racial and ethnic minorities. The goal of this cross-sectional study was to assess the relationship between various immune-related diseases and basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and cutaneous malignant melanoma (MM) diagnoses within the diverse “All of Us” Research Program cohort. It included 307,451 adult participants with electronic health record (EHR) data enrolled from 2017-2023. Participant EHRs were used to determine immune-related diseases and skin cancer diagnoses. Multivariate logistic regression analyses were adjusted for demographic characteristics, lifestyle factors such as smoking history, in addition to health insurance status, educational attainment, history of photosensitivity, history of organ transplant, as well as UV exposure at residence. R v4.0.5 was utilized to conduct the analysis. 8,107 participants with BCC, 4,102 with SCC and 2,461 with MM were identified. Personal history of any immune-related disease was significantly associated with all skin cancer types (odds ratio and 95% confidence intervals -- BCC: 1.27 [1.20-1.33]; SCC:1.38 [1.29-1.49]; MM: 1.55 [1.42-1.69]). Rheumatoid arthritis, hypothyroidism, type I diabetes, Crohn’s disease, and ulcerative colitis were each associated with the three skin cancer types. Multiple sclerosis, Sjogren’s syndrome, hyperthyroidism, dermatomyositis/polymyositis, vitiligo, and psoriasis were associated with at least one skin cancer. Systemic lupus, scleroderma, alopecia areata, and atopic dermatitis were not associated with any skin cancer. In non-White participants alone (n=133,144; 43.3%), history of any immune-related disease was similarly significantly associated with all three skin cancer types. These findings suggest that the history of several immune-related diseases is associated with skin cancer, including among racial/ethnic minorities, and thus increased skin cancer surveillance in these patient populations may be indicated. Citation Format: Erica M. Lin, Sara Ragi, Rachel Lim, Shirley Lin, Isabelle Moseley, Abrar Qureshi, Eunyoung Cho. Immune-related diseases and skin cancer in a racially and ethnically diverse US-based population [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A023.

- 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

Among the different types of skin cancer, melanoma is the one with the highest death rate. Therefore, the early detection of melanoma and the development of technologies that can assist in this task have become significantly important. Convolutional neural networks are one of the most popular skin cancer classification methods. However, most of the available skin cancer datasets include images with lesions that are hard to differentiate from healthy skin or with a high presence of hair that can occlude the lesion. This characteristics of the images makes it harder to extract lesion features. Therefore, utilizing segmentation to extract the lesion location is an important step to reduce hair noise and improve lesion analysis. In this paper, two combining methods for segmentation and classification were explored: concatenation and multiplication. By utilizing these methods, it was possible to improve the accuracy of different neural network architectures by around 1% when compared to unmodified models without segmentation. The best-performing model was selected for further training. This model in conjunction with the segmentation module allowed for the correct re-classification of around 10% of the total examples in the dataset, indicating that a segmentation phase leads to an overall accuracy improvement and suggested that by improving the segmentation, an improvement on the overall accuracy can be obtained.

It has long been recognized that a history of skin cancer puts one at risk for additional primary skin cancers. However, more variable data exists for the risk of developing a non-cutaneous primary cancer following a diagnosis of skin cancer. The data are most variable for Basal Cell Carcinoma (BCC), the most common and least aggressive type of skin cancer. While early studies imply that BCC does not impart a larger risk of other primary non-cutaneous cancers, more recent studies with larger populations suggest otherwise. The cancers most significantly associated with BCC are lip, oropharyngeal, and salivary gland cancer. There is also burgeoning evidence to suggest a link between BCC and prostate, breast, and colorectal cancer, but more data are needed to draw a concrete conclusion. Squamous Cell Carcinoma (SCC), the second most common type of skin cancer, has a slightly more defined risk to other non-cutaneous primary malignancies. There is a notable link between SCC and non-Hodgkin's lymphoma (NHL), possibly due to immunosuppression. There is also an increased risk of other cancers derived from squamous epithelium following SCC, including oropharyngeal, lip, and salivary gland cancer. Some studies also suggest an increased risk of respiratory tract cancer following SCC, possibly due to shared risk factors. Melanoma, a more severe type of skin cancer, shows a well-defined risk of additional primary non-cutaneous malignancies. The most significant of these risks include NHL, thyroid cancer, prostate cancer, and breast cancer along with a host of other cancers. Each of these three main skin cancer types has a profile of genetic mutations that have also been linked to non-cutaneous malignancies. In this review, we discuss a selection of these genes to highlight the complex interplay between different tumorigenesis processes.

Skin cancer is a serious and potentially life-threatening condition, making early and accurate diagnosis essential for effective treatment. The most common types of skin cancer are basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma. Dermatologists rely on skin images to identify the type and severity of these cancers. In recent years, researchers have been developing computer-aided diagnosis systems to assist in early detection. This paper explores a hybrid model that combines machine learning and deep learning techniques to enhance the diagnostic accuracy of skin cancer. Specifically, Convolutional Neural Networks (CNNs) are employed to analyze dermatological images, extracting detailed features that indicate various types of skin cancer. Alongside CNNs, the FractalNet model—a specialized deep learning technique—is used for diagnosis. These models are then integrated with the XGBoost algorithm in parallel. The final diagnosis decision is made through majority voting, ensuring a robust system that can identify both simple and complex features. This hybrid approach, leveraging the different strengths of CNN, FractalNet, and XGBoost, results in a more generalized diagnostic system. The HAM10000 dataset, which includes 10,015 dermatoscopic images of pigmented skin lesions, is used for model training. Data augmentation is applied to balance the dataset. For performance evaluation, precision, recall, F1-score, accuracy, and specificity are measured, with an achieved accuracy of 98.69%..

Abstract: Early detection of skin cancer, including types such as basal cell carcinoma and melanoma, is crucial for effective treatment and reducing mortality rates. Skin cancer diagnoses often surpass those of all other cancers combined, with rising global mortality rates underscoring the need for improved detection methods. Early identification significantly enhances treatment outcomes, as confirmed by the World Health Organization. Advances in image processing and machine vision, particularly through convolutional neural networks (CNNs), have shown that deep learning models can outperform experienced dermatologists in skin cancer detection. In this project, we developed a deep learning model using Python libraries to categorize dermoscopic images from the MNIST HAM-10000 dataset into malignant or benign categories. The model's performance was further evaluated against three datasets from the International Skin Imaging Collaboration (ISIC) challenge archives, with the goal of enhancing early detection and treatment of skin cancer to lower mortality rates

<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>

Early detection and treatment are crucial for recovery from cancer. Due to rapid advancement in medical imaging techniques, today doctors can scan various body parts from different perspectives to diagnose cancer or see the progress during a course of treatment. Such techniques are less painful and require less preparation and recovery. The recent evolution of AI, particularly deep learning algorithms have shown potential in computer-aided diagnosis of cancer. These include analysis of cancer images for detection of tumor region, prognosis, skin tissue inspection, etc. Convolutional Neural Network (CNN) based image processing techniques are showing promising results in the diagnosis of breast cancer, skin cancer, prostate cancer, lung cancer, etc. Literature shows many other CNN-based models such as feed-forward neural network, Deep CNN, Multi-Crop CNN, fully convolutional networks (FCNs), Deep Fully Convolutional Network (DFCNet), Recurrent neural networks, etc. The CNN focus solely on the image-specific features and hence require a lesser number of input parameters. A CNN model reduces the size of the input image vector without losing the features critical for making an accurate prediction. This makes it easier to process and reduces computational complexity. CNNs are also not sensitive to the object positioning in an image since the approximate position of image motifs relative to others is known. CNN deals with the variations in images through augmentation and other pre-processing techniques. This makes them robust to various straining conditions in images. This chapter presents an overview of a generic CNN architecture followed by a discussion on major advances in CNN models and their applications for cancer image analysis.

Cancer is among the common causes of death around the world. Skin cancer is one of the most lethal types of cancer. Early diagnosis and treatment are vital in skin cancer. In addition to traditional methods, method such as deep learning is frequently used to diagnose and classify the disease. Expert experience plays a major role in diagnosing skin cancer. Therefore, for more reliable results in the diagnosis of skin lesions, deep learning algorithms can help in the correct diagnosis. In this study, we propose InSiNet, a deep learning-based convolutional neural network to detect benign and malignant lesions. The performance of the method is tested on International Skin Imaging Collaboration HAM10000 images (ISIC 2018), ISIC 2019, and ISIC 2020, under the same conditions. The computation time and accuracy comparison analysis was performed between the proposed algorithm and other machine learning techniques (GoogleNet, DenseNet-201, ResNet152V2, EfficientNetB0, RBF-support vector machine, logistic regression, and random forest). The results show that the developed InSiNet architecture outperforms the other methods achieving an accuracy of 94.59%, 91.89%, and 90.54% in ISIC 2018, 2019, and 2020 datasets, respectively. Since the deep learning algorithms eliminate the human factor during diagnosis, they can give reliable results in addition to traditional methods.

In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and F-score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.