Highly Accurate Skin Cancer Diagnosis Using HMT-NET and Vision Transformer Models

A novel nonlinear automated multi-class skin lesion detection system using soft-attention based convolutional neural networks

<p>The early and accurate diagnosis of skin cancer is crucial for improving patient outcomes and reducing the need for invasive biopsies. This study proposes a deep learning model for classifying skin malignancy using transfer learning and data augmentation techniques to address limitations observed in previous models and enhance diagnostic accuracy. The approach involves applying transfer learning to a pre-trained ResNet152 architecture using tensorflow and keras. Data augmentation techniques are employed on a dataset consisting of 10,015 skin lesion images obtained from the international skin imaging collaboration (ISIC) 2018 challenge, which encompasses diverse lesion types, sizes, and colors, posing a challenging classification task. Binary cross-entropy serves as the loss function, and the Adam optimizer is utilized for training the model. The results demonstrate a specificity of 87.42% and an F1 score of 0.854, outperforming other models in the field. These statistical findings highlight the effectiveness of transfer learning and data augmentation techniques in improving the accuracy of skin cancer diagnosis. The novelty of this study lies in the combination of transfer learning and data augmentation methods to enhance diagnostic accuracy. However, it is important to acknowledge the limitations of this study, including the necessity for further investigation to evaluate the clinical practicality of the model and address potential biases. Future research could explore the application of this model in a clinical setting and the development of models for detecting other types of skin lesions. In conclusion, the proposed deep learning model based on the ResNet152 architecture showcases promising results in the classification of skin lesions, demonstrating its potential for accurate skin cancer diagnosis. With further research and improvement, these models have the potential to revolutionize healthcare, improving patient outcomes, reducing healthcare costs, and increasing accessibility to screening and diagnosis, particularly for underserved populations.</p>

Skin cancer segmentation and recognition from dermoscopy images: a novel framework based on improved DeepLabV3+ and network-level fused deep architectures.

Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals' visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques' results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.

Skin cancer has emerged as a grave health concern leading to significant mortality rates. Diagnosis of this disease traditionally relies on specialist dermatologists who interpret dermoscopy images using the ABCD rule. However, the integration of computer-aided diagnosis technologies is gaining popularity as a means to assist clinicians in accurate skin cancer diagnosis, overcoming potential challenges associated with human error. The objective of this research is to develop a robust system for the detection of skin cancer by employing machine learning algorithms for skin lesion classification and detection. The proposed system utilizes Convolutional Neural Network (CNN), a highly accurate and efficient deep learning technique well-suited for image classification tasks. By using the power of CNN, this system effectively classifies various skin diseases in dermoscopic images associated with skin cancer The MNIST HAM10000 dataset, comprising 10015 images, serves as the foundation for this study. The dataset encompasses seven distinct skin diseases falling within the realm of skin cancer. In this study, diverse transfer learning methods were used and evaluated to enhance the performance of the system. By comparing and analyzing these approaches the highest accuracy rate was obtained using the MobileNetV2 model with a rate of 80.79% accuracy.

Skin cancer is a common form of cancer that affects millions of people worldwide. Early detection and accurate diagnosis of skin cancer are crucial for effective treatment and management of the disease. There has been a growing interest in using deep learning techniques and computer vision algorithms to develop automated skin cancer detection systems in recent years. Among these techniques, convolutional neural networks (CNN) have shown remarkable performance in detecting and classifying skin lesions. This paper presents a comprehensive study using CNN and deep learning techniques for skin cancer detection using the International Skin Imaging Collaboration (ISIC) dataset. The proposed architecture is a compact deep CNN that is trained using a dataset of benign and malignant skin lesion images. The proposed architecture has achieved 84.8% accuracy, 83.8% TPR, 83.7% TNR, 81.6% F1-score and 80.5% precision for performance evaluation. The experimental results show promising results for the accurate and efficient detection of skin cancer, which has the potential to improve the diagnosis and treatment of this life-threatening disease.

Skin cancer is a significant and life-threatening medical condition caused by abnormal cell growth in the skin. Early detection and accurate diagnosis are critical for effective treatment and improved patient outcomes. However, manual diagnosis of skin cancer can be time-consuming and prone to inter-observer variability, necessitating the development of automated and objective diagnostic approaches. This research purports to diagnose and localize skin cancer automatically utilizing a novel Deep Reinforcement Learning technique. The proposed approach leverages a modified version of the Asynchronous Advantage Actor Critic (A3C) algorithm, comprising multiple independent agents represented as Convolutional Neural Networks (CNNs). These agents interact with the environment (skin images) and perform segmentation actions based on policies that maximize expected rewards, promoting accurate segmentation and penalizing false negatives as well as false positives. The research employs three diverse skin cancer datasets, namely PH2, ISIC 2018, and ISIC 2017 in order to assess the proposed approach’s efficiency. Quantitative evaluation metrics, including Precision, Specificity, Accuracy, Jaccard Index (IoU), Sensitivity, Dice coefficient, and F1-score are used to assess segmentation accuracy. The proposed method is compared with advanced deep-learning algorithms commonly used for skin cancer segmentation. The results demonstrate the superiority of the proposed approach in accurately localizing cancerous regions under various challenging conditions, such as hair presence, image blurriness, and the presence of other objects or obstacles. The proposed approach outperforms existing methods in terms of segmentation accuracy (98.8%) in diverse skin image sources.

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.

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.

Every year, millions of people climb in various states of undress into warm, glowing tanning beds, where during a typical 2- to 15-minute session they’ll absorb a controlled dose of ultraviolet (UV) radiation at an intensity up to two to three times stronger than the sunlight striking the equator at noon. The tanning industry has grown rapidly since the 1980s,1 rising to an estimated 28 million users in the United States.2 This rise has been accompanied by an increase in diagnoses of skin cancer. The reasons behind the rising skin cancer diagnoses remain open to debate. Some experts attribute the rise to more frequent skin cancer screening, whereas others blame environmental and behavioral risk factors, particularly changes in UV exposure. In this latter context, UV-emitting tanning beds—classified as carcinogenic to humans by the International Agency for Research on Cancer (IARC)3—have come under growing scrutiny. People tan to look healthy, but looks can be deceiving; UV radiation causes all three types of skin cancer. Melanoma, a tumor of the cells that produce the skin pigment melanin, is the rarest but deadliest type, accounting for 75% of skin cancer deaths worldwide.4 According to the National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) program, melanoma incidence among U.S. whites (who develop the disease more often than other races) rose from 8.7 cases per 100,000 people in 1975 to 28 cases per 100,000 in 2009.5 Most of that increase occurred in older men, who rarely tan indoors. But a closer look at the age-stratified SEER data reveals that melanoma rates among white girls and women aged 15–39 rose by 3.6% per year between 1992 and 2006, compared with a 2% increase per year among boys and men of the same ages.6 Although they’re not tracked by SEER, squamous cell carcinoma (SCC) and basal cell carcinoma (BCC)—the other two types of skin cancer—also appear to be on the rise, according to regional studies from the United States and Europe. A recent study by Anne Marie Skellett, a consulting dermatologist at Norfolk and Norwich University Hospital, reveals that BCC diagnoses among people under age 30 in the United Kingdom jumped 145% between 1981 and 2006.7 Statistics such as these have prompted 33 U.S. states and some municipalities to ban or restrict indoor tanning among children under age 18.8 California’s ban, signed into law in October 2011, was the first,9 followed by Vermont in April 201210 and the city of Chicago the following June.11 Other states have introduced legislation to limit indoor tanning among minors.8 Melanoma in the United States Scanning electron micrograph of a melanoma cell magnified 8,000 times Mary Brady, an associate professor of surgery at Weill Medical College in New York and the author of an editorial on indoor tanning that appeared in the May 2012 issue of the Journal of Clinical Oncology,12 says the bans make sense. “We legislate against smoking in kids less than 18, and that sends a strong message that there’s something wrong with it,” she says. “We need to send the same message on indoor tanning.” But the bans have drawn a backlash from the tanning bed industry, whose representatives say they’ve been unfairly and incorrectly singled out. John Overstreet, executive director at the Indoor Tanning Association in Washington, DC, describes the evidence linking indoor tanning to skin cancer as speculation and advocacy science reported by the media as fact. He points out that UV light triggers skin cells to produce vitamin D, which may have cancer-protective effects. “It’s frustrating,” he says. “There’s no doubt that repeated overexposure to UV or burning can cause skin problems, but you also have to look at the health benefits, and that issue always gets lost.”

Simple SummaryWhile melanoma accounts for 4% of skin cancer cases, it causes 75% of skin-cancer-related deaths. The survival rate for melanoma is higher for early-identified cases, so improved access to diagnosis and screening programs is essential for addressing skin cancer deaths. Computer-aided diagnosis utilizing machine learning can be used to differentiate malignant and benign skin lesions. There is significant research into the use of convolutional neural networks to classify skin lesions from dermoscopic images. However, to provide cost-effective and accessible options for early detection of malignant melanoma, smartphone applications capable of accurately classifying skin lesions from images taken on a smartphone would be beneficial. This research investigates a previously underexplored dataset of smartphone images and develops a novel multi-layer deep convolutional neural network model, named SkinLesNet, to classify three types of skin lesions, including melanoma. Further studies to validate the model should be conducted as other image datasets become available.Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet’s performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet’s effectiveness, as it consistently outperformed both benchmarks across all datasets.

Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and patient survival rates. Nonetheless, the inherent visual similarities between benign and malignant lesions pose substantial challenges to accurate classification. To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks and separable self-attention mechanisms, tailored to enhance feature extraction and optimize classification performance. The inclusion of ConvNeXtV2 blocks in the initial two stages is driven by their ability to effectively capture fine-grained local features and subtle patterns, which are critical for distinguishing between visually similar lesion types. Meanwhile, the adoption of separable self-attention in the later stages allows the model to selectively prioritize diagnostically relevant regions while minimizing computational complexity, addressing the inefficiencies often associated with traditional self-attention mechanisms. The model was comprehensively trained and validated on the ISIC 2019 dataset, which includes eight distinct skin lesion categories. Advanced methodologies such as data augmentation and transfer learning were employed to further enhance model robustness and reliability. The proposed architecture achieved exceptional performance metrics, with 93.48% accuracy, 93.24% precision, 90.70% recall, and a 91.82% F1-score, outperforming over ten Convolutional Neural Network (CNN) based and over ten Vision Transformer (ViT) based models tested under comparable conditions. Despite its robust performance, the model maintains a compact design with only 21.92 million parameters, making it highly efficient and suitable for model deployment. The Proposed Model demonstrates exceptional accuracy and generalizability across diverse skin lesion classes, establishing a reliable framework for early and accurate skin cancer diagnosis in clinical practice.

Accurate segmentation of skin lesions from dermatological images is essential for the early detection of melanoma and other skin cancers. Conventional methods based on convolutional neural networks (CNNs) and transformer architectures often struggle to capture both local and global contextual features, delineate irregular lesion boundaries, and remain robust against artifacts such as hair, shadows, and illumination variations. To overcome these challenges, we introduce LeSegGAN, a hybrid attention-enhanced generative adversarial network (GAN) framework for robust skin lesion segmentation. The generator combines convolutional and inception modules with residual connections and channel attention to extract multi-scale features, while a vision transformer (ViT)-based discriminator improves segmentation accuracy through adversarial learning. A composite loss function integrating weighted binary cross-entropy, Dice, and focal losses further addresses class imbalance and enhances performance. LeSegGAN is evaluated on four benchmark datasets namely, Waterloo skin cancer, MED-NODE, SD-260, and ISIC-2016. The proposed LeSegGAN consistently outperformed five state-of-the-art deep learning models (UNet, UNet++, SegNet, FCN, and DTP-Net), achieving accuracies of 0.9943, 0.9759, 0.9873, and 0.9724, with corresponding IoU scores of 0.9451, 0.9664, 0.8709, and 0.7717. These results highlight LeSegGAN’s strong generalization ability and robustness, demonstrating its potential for integration into computer-aided diagnostic systems for automated skin cancer detection.

Skin Cancer accounts for one-third of all diagnosed cancers worldwide. The prevalence of skin cancers have been rising over the past decades. In recent years, use of dermoscopy has enhanced the diagnostic capability of skin cancer. The accurate diagnosis of skin cancer is challenging for dermatologists as multiple skin cancer types may appear similar in appearance. The dermatologists have an average accuracy of 62% to 80% in skin cancer diagnosis. The research community has been made significant progress in developing automated tools to assist dermatologists in decision making. In this work, we propose an automated computer-aided diagnosis system for multi-class skin (MCS) cancer classification with an exceptionally high accuracy. The proposed method outperformed both expert dermatologists and contemporary deep learning methods for MCS cancer classification. We performed fine-tuning over seven classes of HAM10000 dataset and conducted a comparative study to analyse the performance of five pre-trained convolutional neural networks (CNNs) and four ensemble models. The maximum accuracy of 93.20% for individual model amongst the set of models whereas maximum accuracy of 92.83% for ensemble model is reported in this paper. We propose use of ResNeXt101 for the MCS cancer classification owing to its optimized architecture and ability to gain higher accuracy.