Benign Keratosis Research Articles

A Deep Learning Framework for Multi-Class Skin Cancer Diagnosis: Addressing Class Imbalance and Performance Optimization Through Convolutional Neural Network Architectures

Skin cancer, particularly melanoma, is one of the most fatal forms of cancer worldwide, necessitating early detection for effective treatment. The HAM10000 dataset is used as a standard for skin lesion analysis in this study to use a Convolutional Neural Network (CNN) to sort seven different types of skin cancer into groups. The dataset consists of over 10,000 labeled dermoscopic images representing various classes such as melanoma, basal cell carcinoma, and benign keratosis. The methodology includes preprocessing techniques like resizing, normalization, and augmentation to enhance the diversity of the data and address class imbalance. The Adam optimizer, with a learning rate of 0.0001, trained a custom CNN model with multiple convolutional layers, batch normalization, and ReLU activations. The model’s performance was evaluated across training, validation, and testing subsets, achieving a training accuracy of 98.17% and a validation accuracy of 99.60% after 10 epochs. Evaluation tools, such as a classification report and a confusion matrix, showed high accuracy for common classes like melanocytic nevi but showed mistakes in underrepresented classes like vascular lesions and dermatofibroma. Compared with state-of-the-art techniques like ResNet and EfficientNet, the proposed model demonstrates competitive performance with fewer computational resources. The study emphasizes the potential of deep learning in automating skin cancer detection while identifying challenges like class imbalance and the need for improved generalization. The results show how important advanced augmentation techniques, transfer learning, and ensemble approaches are for making models work better. This research contributes to the ongoing development of reliable, automated diagnostic tools, aiming to improve clinical outcomes and assist dermatologists in accurate and efficient skin cancer diagnosis.

Skin Cancer Detection Using Machine Learning

Skin-related issues often serve as indicators of underlying health problems in other parts of the human body, adversely affecting an individual's overall fitness and well-being. However, such issues are frequently ignored, as they are perceived to be either painless or have minimal to no impact on daily life. To address this challenge, this system was developed with the aim of early detection of skin cancer. The system enables users to upload images or videos of the affected area in real-time, utilizing a skin cancer detector to identify existing conditions, determine the cancer type (if present), and provide instant feedback. The system is precisely configured to deliver highperformance results, offering real-time recommendations for medications or treatments based on its findings. By reducing the stress and inconvenience associated with hospital visits for dermatological consultations, this system empowers individuals to identify skin-related issues accurately and make informed decisions about seeking further medical attention from a qualified dermatologist. Powered by image processing using OpenCV, a convolutional neural network (CNN), and machine learning techniques, the system excels in recognizing various skin conditions with high accuracy. It can detect conditions such as nevus, vascular lesions, seborrheic keratosis, basal cell carcinoma, melanoma, pigmented benign keratosis, squamous cell carcinoma, dermatofibroma, and actinic keratosis, while also recommending appropriate remedial health solutions.

SkinSage XAI: An explainable deep learning solution for skin lesion diagnosis.

Skin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black-box nature of many models limits interpretability, posing challenges for dermatologists. To address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization. A data set of around 50,000 images from the Customized HAM10000, selected for diversity, serves as the foundation. The Inception v3 model is used for classification, supported by gradient-weighted class activation mapping and local interpretable model-agnostic explanations algorithms, which provide clear visual explanations for model outputs. SkinSage XAI demonstrated high performance, accurately categorizing seven types of skin lesions-dermatofibroma, benign keratosis, melanocytic nevus, vascular lesion, actinic keratosis, basal cell carcinoma, and melanoma. It achieved an accuracy of 96%, with precision at 96.42%, recall at 96.28%, f 1 score at 96.14%, and an area under the curve of 99.83%. SkinSage XAI represents a significant advancement in dermatology and artificial intelligence by bridging gaps in accuracy and explainability. The system provides transparent, accurate diagnoses, improving decision-making for dermatologists and potentially enhancing patient outcomes.

Transforming Skin Cancer Diagnosis: A Deep Learning Approach with the Ham10000 Dataset

Skin cancer (SC) is one of the three most common cancers worldwide. Melanoma has the deadliest potential to spread to other parts of the body among all SCs. For SC treatments to be effective, early detection is essential. The high degree of similarity between tumor and non-tumors makes SC diagnosis difficult even for experienced doctors. To address this issue, authors have developed a novel Deep Learning (DL) system capable of automatically classifying skin lesions into seven groups: actinic keratosis (AKIEC), melanoma (MEL), benign keratosis (BKL), melanocytic Nevi (NV), basal cell carcinoma (BCC), dermatofibroma (DF), and vascular (VASC) skin lesions. Authors introduced the Multi-Grained Enhanced Deep Cascaded Forest (Mg-EDCF) as a novel DL model. In this model, first, researchers utilized subsampled multigrained scanning (Mg-sc) to acquire micro features. Second, authors employed two types of Random Forest (RF) to create input features. Finally, the Enhanced Deep Cascaded Forest (EDCF) was utilized for classification. The HAM10000 dataset was used for implementing, training, and evaluating the proposed and Transfer Learning (TL) models such as ResNet, AlexNet, and VGG16. During the validation and training stages, the performance of the four networks was evaluated by comparing their accuracy and loss. The proposed method outperformed the competing models with an average accuracy score of 98.19%. Our proposed methodology was validated against existing state-of-the-art algorithms from recent publications, resulting in consistently greater accuracies than those of the classifiers.

Categorical classification of skin cancer using a weighted ensemble of transfer learning with test time augmentation

Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans. It usually appears in locations that are exposed to the sun, but can also appear in areas that are not regularly exposed to the sun. Due to the striking similarities between benign and malignant lesions, skin cancer detection remains a problem, even for expert dermatologists. Considering the inability of dermatologists to diagnose skin cancer accurately, a convolutional neural network (CNN) approach was used for skin cancer diagnosis. However, the CNN model requires a significant number of image datasets for better performance; thus, image augmentation and transfer learning techniques have been used in this study to boost the number of images and the performance of the model, because there are a limited number of medical images. This study proposes an ensemble transfer-learning-based model that can efficiently classify skin lesions into one of seven categories to aid dermatologists in skin cancer detection: (i) actinic keratoses, (ii) basal cell carcinoma, (iii) benign keratosis, (iv) dermatofibroma, (v) melanocytic nevi, (vi) melanoma, and (vii) vascular skin lesions. Five transfer learning models were used as the basis of the ensemble: MobileNet, EfficientNetV2B2, Xception, ResNext101, and DenseNet201. In addition to the stratified 10-fold cross-validation, the results of each individual model were fused to achieve greater classification accuracy. An annealing learning rate scheduler and test-time augmentation (TTA) were also used to increase the performance of the model during the training and testing stages. A total of 10,015 publicly available dermoscopy images from the HAM10000 (Human Against Machine) dataset, which contained samples from the seven common skin lesion categories, were used to train and evaluate the models. The proposed technique attained 94.49% accuracy on the dataset. These results suggest that this strategy can be useful for improving the accuracy of skin cancer classification. However, the weighted average of f1-score, recall, and precision were obtained to be 94.68%, 94.49%, and 95.07%, respectively.

YOLOSkin: A fusion framework for improved skin cancer diagnosis using YOLO detectors on Nvidia Jetson Nano

Skin cancer stands out as one of the most common and lethal forms of cancer, characterized by the rapid and uncontrolled division of the cells comprising the skin layer. Early diagnosis is crucial for reducing fatality rates. However, accurately identifying different types of tumorous cells poses challenges, leading to potential misdiagnoses by physicians. To aid clinicians in precise cancer identification, this study proposes a comprehensive framework incorporating the latest compact versions of YOLO, including YOLOv3tiny, YOLOv4tiny, YOLOv5s, YOLOv7tiny, and YOLOv8s. The research focuses on detecting and classifying nine types of skin cancer using ISIC datasets: Actinic Keratosis, Basal Cell Carcinoma, Dermatofibroma, Melanoma, Nevus, Pigmented Benign Keratosis, Seborrheic Keratosis, Squamous Cell Carcinoma, and Vascular Lesion. Results indicate that YOLOv5s performs well for certain cancer classes, while YOLOv8s excels in others. To enhance the overall performance, a fusion strategy is employed, integrating predictions from both YOLOv5s and YOLOv8s based on confidence scores. The experiments demonstrate that the overall detection accuracy improves from 91.5 % to 94.3 % in terms of mean average precision (mAP@0.5) and 89.6 % to 97.87 % in terms of precision. To implement an embedded deep skin cancer detection system (ESCDS), the suggested framework utilizes the edge computing device, Nvidia Jetson Nano to assess real-time performance and efficiency of lightweight YOLO detectors. For single-image prediction, the approximate inference time on the edge computing device is 106.5 ms for YOLOv3tiny, 125.6 ms for YOLOv4tiny, 142.5 ms for YOLOv5s, 13.9 ms for YOLOv7tiny, and 35.4 ms for YOLOv8s, respectively.

Optimized compact fortified weight-prioritized convolutional network for swift skin lesion identification using dermoscopic images

Skin cancer is a leading cause of cancer-related mortality, posing a significant global health challenge. Early detection and treatment are crucial for survival rates. While dermoscopy is a valuable non-invasive imaging tool for diagnosing skin lesions, its reliance on the expertise of dermatologists introduces variability, affecting diagnostic reliability. Existing deep learning models for skin lesion analysis often prioritize accuracy over computational efficiency, limiting their practical application in clinical settings where both rapidity and precision are crucial. To address these limitations, this study proposes a novel model called the Compact Fortified Weight-Prioritized Convolutional Network (CWCN), optimized using the Harbor Seal Whiskers Optimization (HOA) algorithm (CWCN-HOA-SLD-DI). The CWCN is designed to offer a balance between high performance and computational efficiency. Initially, dermoscopic images from the ISIC Archive dataset are collected and subjected to a series of preprocessing steps, including image augmentation to enhance robustness, normalization using log-sinh with Adaptive Box-Cox transformation, and noise removal employing Guided Box Filtering (GBF) and Guided Image Filtering (GIF). The CWCN-HOA framework is then utilized to classify skin lesions into categories such as Malignant, Melanocytic Nevus, Basal Cell Carcinoma, Actinic Keratosis, Benign Keratosis, Dermatofibroma, and Vascular Lesion. The proposed CWCN-HOA-SLD-DI model is implemented in Python, and its performance is evaluated against current methods. The results indicate that the CWCN-HOA approach achieves significant improvements in both classification accuracy as 99.98% and computational efficiency as 92ms. By offering a combination of high performance and computational efficiency, the CWCN-HOA model represents a promising solution for accurate and efficient skin cancer detection. Its potential to improve the diagnostic capabilities of dermatologists and enhance patient outcomes underscores its significance in addressing this critical global health issue.

A Hybrid Convolutional Neural Network Model for the Classification of Multi‐Class Skin Cancer

ABSTRACTSkin cancer is a significant public health issue, making accurate and early diagnosis crucial. This study proposes a novel and efficient hybrid deep‐learning model for accurate skin cancer diagnosis. The model first employs DeepLabV3+ for precise segmentation of skin lesions in dermoscopic images. Feature extraction is then carried out using three pretrained models: MobileNetV2, EfficientNetB0, and DenseNet201 to ensure balanced performance and robust feature learning. These extracted features are then concatenated, and the ReliefF algorithm is employed to select the most relevant features. Finally, obtained features are classified into eight categories: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, melanocytic nevus, squamous cell carcinoma, and vascular lesion using the kNN algorithm. The proposed model achieves an F1 score of 93.49% and an accuracy of 94.42% on the ISIC‐2019 dataset, surpassing the best individual model, EfficientNetB0, by 1.20%. Furthermore, the evaluation of the PH2 dataset yielded an F1 score of 94.43% and an accuracy of 94.44%, confirming its generalizability. These findings signify the potential of the proposed model as an expedient, accurate, and valuable tool for early skin cancer detection. They also indicate combining different CNN models achieves superior results over the results obtained from individual models.

Enhancing Skin Disease Diagnosis Through Deep Learning: A Comprehensive Study on Dermoscopic Image Preprocessing and Classification

ABSTRACTSkin cancer occurs when abnormal cells in the top layer of the skin, known as the epidermis, undergo uncontrolled growth due to unrepaired DNA damage, leading to the development of mutations. These mutations lead to rapid cell growth and development of cancerous tumors. The type of cancerous tumor depends on the cells of origin. Overexposure to ultraviolet rays from the sun, tanning beds, or sunlamps is a primary factor in the occurrence of skin cancer. Since skin cancer is one of the most common types of cancer and has a high mortality, early diagnosis is extremely important. The dermatology literature has many studies of computer‐aided diagnosis for early and highly accurate skin cancer detection. In this study, the classification of skin cancer was provided by Regnet x006, EfficientNetv2 B0, and InceptionResnetv2 deep learning methods. To increase the classification performance, hairs and black pixels in the corners due to the nature of dermoscopic images, which could create noise for deep learning, were eliminated in the preprocessing step. Preprocessing was done by hair removal, cropping, segmentation, and applying a median filter to dermoscopic images. To measure the performance of the proposed preprocessing technique, the results were obtained with both raw images and preprocessed images. The model developed to provide a solution to the classification problem is based on deep learning architectures. In the four experiments carried out within the scope of the study, classification was made for the eight classes in the dataset, squamous cell carcinoma and basal cell carcinoma classification, benign keratosis and actinic keratosis classification, and finally benign and malignant disease classification. According to the results obtained, the best accuracy values of the experiments were obtained as 0.858, 0.929, 0.917, and 0.906, respectively. The study underscores the significance of early and accurate diagnosis in addressing skin cancer, a prevalent and potentially fatal condition. The primary aim of the preprocessing procedures was to attain enhanced performance results by concentrating solely on the area spanning the lesion instead of analyzing the complete image. Combining the suggested preprocessing strategy with deep learning techniques shows potential for enhancing skin cancer diagnosis, particularly in terms of sensitivity and specificity.

A Method of Skin Disease Detection Using Image Processing and Machine Learning

Skin conditions are prevalent health issues globally. The dangers posed by these infections are unseen, leading to physical discomfort and mental anguish. In severe cases, skin diseases can even progress to skin cancer. Consequently, the identification of skin diseases from clinical images remains a significant challenge in medical image analysis. Furthermore, manual diagnosis by medical professionals is time-consuming and subjective. Therefore, there is a need for automated skin disease prediction to expedite treatment planning for both patients and dermatologists. These studies presents a digital hair removal method and de-blurs or denoise the images. To extract essential patterns from the skin imagesand statistical features techniques are utilized. Two efficient machine learning algorithms, namely Support Vector Machine (SVM), and Convolutional Neural Network (CNN) classifiers, are employed with the extracted features to accurately classify skin images as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and Squamous cell carcinoma (SCC). The models are validated using the ISIC 2019 challenge and HAM10000 datasets, with SVM demonstrating slightly superior performance compared to the other classifiers. Furthermore, a comparison with state-of-the-art methods is conducted to evaluate the effectiveness of the proposed approach.

Extraction of Shape and Texture Features of Dermoscopy Image for Skin Cancer Identification

Skin diseases are increasing and becoming a very serious problem. Skin cancer in general there are 2, namely melanoma and non-melanoma. Cases that are often encountered are in non-melanoma types. A critical factor in the treatment of skin cancer is early diagnosis. Doctors usually use the biopsy method to detect skin cancer. Computer-based technology provides convenient, cheaper, and faster diagnosis of skin cancer symptoms. This study aims to identify the type of skin cancer. The data used in the study were 6 types of skin cancer, namely Basal Cell Carcinoma, Dermatofibroma, Melanoma, Nevus image, Pigmented Benign Keratosis image, or Vascular Lesion, with a total of 60 dermoscopy images obtained from the Kaggle site. Dermoscopy image processing begins with a pre-processing process, which converts RGB images to LAB. After that, segmentation is carried out to separate objects from the background. The method of extracting shape and texture features is used to obtain the characteristics of dermoscopy images. As many as 2 types of shape features, namely eccentricity and metric, and 4 types of texture features, namely contrast, correlation, energy, and homogeneity. The result of this study is that it can identify the type of skin cancer based on image features that have been extracted using a program from the Matlab application. The technique of extracting shape and texture features is proven to work well in identifying the type of skin cancer. In the future it is expected to use more data, and add color features in identifying dermoscopy images.

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

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.

Optimized self-attention based cycle-consistent generative adversarial network adopted melanoma classification from dermoscopic images.

Skin is the exposed part of the human body that constantly protected from UV rays, heat, light, dust, and other hazardous radiation. One of the most dangerous illnesses that affect people is skin cancer. A type of skin cancer called melanoma starts in the melanocytes, which regulate the colour in human skin. Reducing the fatality rate from skin cancer requires early detection and diagnosis of conditions like melanoma. In this article, a Self-attention based cycle-consistent generative adversarial network optimized with Archerfish Hunting Optimization Algorithm adopted Melanoma Classification (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Primarily, the input Skin dermoscopic images are gathered via the dataset of ISIC 2019. Then, the input Skin dermoscopic images is pre-processed using adjusted quick shift phase preserving dynamic range compression (AQSP-DRC) for removing noise and increase the quality of Skin dermoscopic images. These pre-processed images are fed to the piecewise fuzzy C-means clustering (PF-CMC) for ROI region segmentation. The segmented ROI region is supplied to the Hexadecimal Local Adaptive Binary Pattern (HLABP) to extract the Radiomic features, like Grayscale statistic features (standard deviation, mean, kurtosis, and skewness) together with Haralick Texture features (contrast, energy, entropy, homogeneity, and inverse different moments). The extracted features are fed to self-attention based cycle-consistent generative adversarial network (SACCGAN) which classifies the skin cancers as Melanocytic nevus, Basal cell carcinoma, Actinic Keratosis, Benign keratosis, Dermatofibroma, Vascular lesion, Squamous cell carcinoma and melanoma. In general, SACCGAN not adapt any optimization modes to define the ideal parameters to assure accurate classification of skin cancer. Hence, Archerfish Hunting Optimization Algorithm (AHOA) is considered to maximize the SACCGAN classifier, which categorizes the skin cancer accurately. The proposed method attains 23.01%, 14.96%, and 45.31% higher accuracy and 32.16%, 11.32%, and 24.56% lesser computational time evaluated to the existing methods, like melanoma prediction method for unbalanced data utilizing optimized Squeeze Net through bald eagle search optimization (CNN-BES-MC-DI), hyper-parameter optimized CNN depending on Grey wolf optimization algorithm (CNN-GWOA-MC-DI), DEANN incited skin cancer finding depending on fuzzy c-means clustering (DEANN-MC-DI). RESEARCH HIGHLIGHTS: This manuscript, self-attention based cycle-consistent. SACCGAN-AHOA-MC-DI method is implemented in Python. (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Adjusted quick shift phase preserving dynamic range compression (AQSP-DRC). Removing noise and increase the quality of Skin dermoscopic images.

Multi- classification of skin lesions using a deep learning-based convolutional neural network

Skin lesions refer to changes or abnormalities on the skin, such as moles, freckles, cysts, warts, and more. While not all skin lesions are cancerous, some types can indicate or develop into skin cancer. Skin cancer has been on the rise in recent years, causing many deaths. However, manual examination by dermatologists is expensive and time-consuming. Research in this field has focused on developing an automated system to detect skin cancer and reduce the associated mortality rate. However, the performance of such systems needs improvement. In this study, we propose a Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors (SMOTEENN) to augment the data, resulting in a more balanced dataset. Then, we used a Convolutional Neural Network (CNN) model on the benchmark HAM10000 dataset. The dataset comprises seven skin diseases, including Actinic Keratoses, Basal cell carcinoma, Benign keratosis, Dermatofibroma, Melanocytic nevi, Melanoma, and Vascular skin lesions. With a balanced dataset, the proposed model achieves an accuracy, precision, recall, and f1-score of 99.35%, 99.00%, 99.00%, and 99.00%, respectively. The balanced dataset performs 22% better than an unbalanced dataset.

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.

The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions. In the first stage, a CNN model was developed to classify skin lesion images into two classes, namely benign and malignant. In the second stage, the model was then used with the transfer learning concept to further categorize benign lesions into five subcategories (melanocytic nevus, actinic keratosis, benign keratosis, dermatofibroma, and vascular) and malignant lesions into two subcategories (melanoma and basal cell carcinoma). The frozen weights of the CNN developed-trained with correlated images benefited the transfer learning using the same type of images for the subclassification of benign and malignant classes. The proposed multistage and multiclass technique improved the classification accuracy of the online ISIC2018 skin lesion dataset by up to 93.4% for benign and malignant class identification. Furthermore, a high accuracy of 96.2% was achieved for subclassification of both classes. Sensitivity, specificity, precision, and F1-score metrics further validated the effectiveness of the proposed multistage and multiclass framework. Compared to existing CNN models described in the literature, the proposed approach took less time to train and had a higher classification rate.

A Convolutional Neural Network Based on Soft Attention Mechanism and Multi-Scale Fusion for Skin Cancer Classification

The seven most common skin diseases are melanocytic nevus, melanoma, benign keratosis, basal cell carcinoma, actinic keratosis, vascular lesions, and dermatofibroma. Among them, melanoma has been identified as one of the deadliest cancers based on medical studies and research. The current trend in disease detection revolves around the use of machine learning and deep learning models. Regardless of the model used, the crucial aspect is achieving accurate classification for these diseases. With the emergence of powerful convolutional neural networks (CNNs), significant progress has been made in classification of skin cancer lesions in recent years. However, various challenges hinder the development of practical and effective solutions. First, due to the specific nature of skin cancer lesion images, the current deep neural network architectures and training strategies have poor adaptability to medical images. They are also prone to gradient vanishing issues during network iteration, which hinders the construction of high-performance deep learning models that leverage distinctive characteristics of skin lesion images. Second, there exists a discordance between skin lesion images and deep learning network structures. To address these issues, this study introduces a soft attention mechanism to enhance adaptability to skin cancer lesion images and improve the extraction of informative features from medical images. Additionally, a novel multi-scale fusion convolutional neural network model is proposed to overcome the mismatch between deep learning CNN architectures and skin lesion images. This model autonomously extracts appearance features from raw dermatological medical images. Comparisons with other popular techniques demonstrate the effectiveness of the proposed model, which can achieve an accuracy of 93.9% on HAM10000 dataset. There is ongoing research to overcome the remaining challenges and further enhance the performance of skin cancer classification algorithms.

Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures

This manuscript proposes the possibility of concatenated signatures (instead of images) obtained from different integral transforms, such as Fourier, Mellin, and Hilbert, to classify skin lesions. Eight lesions were analyzed using some algorithms of artificial intelligence: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma (MEL), actinic keratosis (AK), benign keratosis (BKL), dermatofibromas (DF), melanocytic nevi (NV), and vascular lesions (VASCs). Eleven artificial intelligence models were applied so that eight skin lesions could be classified by analyzing the signatures of each lesion. The database was randomly divided into 80% and 20% for the training and test dataset images, respectively. The metrics that are reported are accuracy, sensitivity, specificity, and precision. Each process was repeated 30 times to avoid bias, according to the central limit theorem in this work, and the averages and ± standard deviations were reported for each metric. Although all the results were very satisfactory, the highest average score for the eight lesions analyzed was obtained using the subspace k-NN model, where the test metrics were 99.98% accuracy, 99.96% sensitivity, 99.99% specificity, and 99.95% precision.

Detection of Malignant Skin Lesions Based on Decision Fusion of Ensembles of Neural Networks.

Today, skin cancer, and especially melanoma, is an increasing and dangerous health disease. The high mortality rate of some types of skin cancers needs to be detected in the early stages and treated urgently. The use of neural network ensembles for the detection of objects of interest in images has gained more and more interest due to the increased performance of the results. In this sense, this paper proposes two ensembles of neural networks, based on the fusion of the decisions of the component neural networks for the detection of four skin lesions (basal cancer cell, melanoma, benign keratosis, and melanocytic nevi). The first system is based on separate learning of three neural networks (MobileNet V2, DenseNet 169, and EfficientNet B2), with multiple weights for the four classes of lesions and weighted overall prediction. The second system is made up of six binary models (one for each pair of classes) for each network; the fusion and prediction are conducted by weighted summation per class and per model. In total, 18 such binary models will be considered. The 91.04% global accuracy of this set of binary models is superior to the first system (89.62%). Separately, only for the binary classifications within the system was the individual accuracy better. The individual F1 score for each class and the global system varied from 81.36% to 94.17%. Finally, a critical comparison is made with similar works from the literature.

MSCDNet-based multi-class classification of skin cancer using dermoscopy images.

Skin cancer is a life-threatening disease, and early detection of skin cancer improves the chances of recovery. Skin cancer detection based on deep learning algorithms has recently grown popular. In this research, a new deep learning-based network model for the multiple skin cancer classification including melanoma, benign keratosis, melanocytic nevi, and basal cell carcinoma is presented. We propose an automatic Multi-class Skin Cancer Detection Network (MSCD-Net) model in this research. The study proposes an efficient semantic segmentation deep learning model "DenseUNet" for skin lesion segmentation. The semantic skin lesions are segmented by using the DenseUNet model with a substantially deeper network and fewer trainable parameters. Some of the most relevant features are selected using Binary Dragonfly Algorithm (BDA). SqueezeNet-based classification can be made in the selected features. The performance of the proposed model is evaluated using the ISIC 2019 dataset. The DenseNet connections and UNet links are used by the proposed DenseUNet segmentation model, which produces low-level features and provides better segmentation results. The performance results of the proposed MSCD-Net model are superior to previous research in terms of effectiveness and efficiency on the standard ISIC 2019 dataset.

SKIN CANCER CLASSIFICATION: A DEEP LEARNING APPROACH

Skin diseases are common in human beings because of significant changes in surrounding environments.The most of these diseases are curable if diagnosed at initial stages. Therefore, earlydiagnosis can spare people’s precious lives. To address these issues, we proposed a novel model based on deep learning to diagnose the skin disease at a preliminary stage using classification. The developedmodel correctly identifies six different skin diseases namely, actinic keratosis, benign keratosis, melanoma,basal cell carcinoma, insects bite and skin acne. Several state-of-the-art algorithms are examinedon benchmark datasets (International Skin Imaging Collaboration (ISIC) 2019 dataset andUCI Data Center) for accuracy, precision, recall and F1-score metrics. The results show that convolutionalneural network (CNN) has a distinct superiority over its peers with accuracy rate of97%, precision 91%, recall 91% and F1-score 91%. This system will provide skin care handlingservices that are precise and accurate and help the dermatologist in early diagnosis of skin diseases