Skin Cancer Detection Research Articles

Skin Imaging Using Optical Coherence Tomography and Photoacoustic Imaging: A Mini-Review

This article provides an overview of the progress made in skin imaging using two emerging imaging modalities, optical coherence tomography (OCT) and photoacoustic imaging (PAI). Over recent years, these technologies have significantly advanced our understanding of skin structure and function, offering non-invasive and high-resolution insights previously unattainable. The review begins by briefly describing the fundamental principles of how OCT and PAI capture images. It then explores the evolving applications of OCT in dermatology, ranging from diagnosing skin disorders to monitoring treatment responses. This article continues by briefly describing the capabilities of PAI imaging, and how PAI has been used for melanoma and non-melanoma skin cancer detection and characterization, vascular imaging, and more. The third section describes the development of multimodal skin imaging systems that include OCT, PAI, or both modes. A comparative analysis between OCT and PAI is presented, elucidating their respective strengths, limitations, and synergies in the context of skin imaging.

Combining Reflectance Confocal Microscopy, Optical Coherence Tomography and Ex-Vivo Fluorescence Confocal Microscopy for Margin Assessment in Basal Cell Carcinoma Excision.

Recent developments of noninvasive, high-resolution imaging techniques, such as reflectance confocal microscopy (RCM) and optical coherence tomography (OCT), have enhanced skin cancer detection and precise tumor excision particularly in highly aggressive and poorly defined basal cell carcinomas (BCCs). The aim of this pilot study is to assess the feasibility and reproducibility of a systematic clinical workflow combining noninvasive (RCM-OCT) and invasive fluorescence confocal microscopy (FCM) imaging modalities in pre- and intra-surgical evaluations of the lateral and deep margins of BCC. Superficial incisions were made 2 mm beyond the clinical-dermoscopic BCC margins. Lateral margins were then explored with OCT and RCM. In positive margins, a further cut was made 2 mm distal from the previous. A final RCM/OCT-based double-negative margin was drawn around the entire perimeter of the lesion before referring to surgery. The freshly excised specimen was then examined with FCM (ex-vivo) for the evaluation of the deep margin. Histopathologic examination eventually confirmed margin involvement. The study included 22 lesions from 13 patients. At the end of the study, 146 margins-106 negative (73%) and 40 positive (27%) at RCM/OCT-were collected. The RCM/OCT margin evaluation showed an overall sensitivity of 100% and a specificity of 96.3%. The overall positive margins diagnostic accuracy was 98.2%. Reproducibility was evaluated on recorded images and the raters showed a substantial inter-observer agreement on both RCM (κ = 0.752) and OCT images (κ = 0.724). The combined RCM/OCT/FCM ex-vivo approach noninvasively facilitates the presurgical and intrasurgical lateral and deep margin assessment of poorly defined BCCs.

Transformer-based framework for multi-class segmentation of skin cancer from histopathology images.

Non-melanoma skin cancer comprising Basal cell carcinoma (BCC), Squamous cell carcinoma (SCC), and Intraepidermal carcinoma (IEC) has the highest incidence rate among skin cancers. Intelligent decision support systems may address the issue of the limited number of subject experts and help in mitigating the parity of health services between urban centers and remote areas. In this research, we propose a transformer-based model for the segmentation of histopathology images not only into inflammation and cancers such as BCC, SCC, and IEC but also to identify skin tissues and boundaries that are important in decision-making. Accurate segmentation of these tissue types will eventually lead to accurate detection and classification of non-melanoma skin cancer. The segmentation according to tissue types and their visual representation before classification enhances the trust of pathologists and doctors being relatable to how most pathologists approach this problem. The visualization of the confidence of the model in its prediction through uncertainty maps is also what distinguishes this study from most deep learning methods. The evaluation of proposed system is carried out using publicly available dataset. The application of our proposed segmentation system demonstrated good performance with an F1 score of 0.908, mean intersection over union (mIoU) of 0.653, and average accuracy of 83.1%, advocating that the system can be used as a decision support system successfully and has the potential of subsequently maturing into a fully automated system. This study is an attempt to automate the segmentation of the most occurring non-melanoma skin cancer using a transformer-based deep learning technique applied to histopathology skin images. Highly accurate segmentation and visual representation of histopathology images according to tissue types by the proposed system implies that the system can be used for skin-related routine pathology tasks including cancer and other anomaly detection, their classification, and measurement of surgical margins in the case of cancer cases.

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases’ diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer’s diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Enhancing Skin Cancer Detection Through Deep Learning Techniques

Skin cancer is a common and possibly deadly disease that affects the skin's outer layers. Promoting knowledge of skin cancer, its risk factors, and the need for early detection can help combat the illness and reduce its impact on individuals and communities around the world. In this project, we describe a novel use of deep learning techniques to detect skin cancer in its early stages using dermatoscopic images. The primary goal of this study is to create an accurate and reliable model for predicting various types of skin cancer, including actinic keratoses and intraepithelial carcinoma (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv), and pyogenic granulomas and hemorrhage (vasc). The project is written in Python, and the primary algorithm/model used is the Convolutional Neural Network (CNN) architecture. CNNs are ideal for image classification jobs because they can automatically learn relevant characteristics from input. Using CNNs, our model is trained on the HAM10000 (Human Against Machine with 10,000 training images) dataset, which contains 10015 high-resolution dermatoscopic images obtained from distinct populations and acquired via multiple modalities. The achieved findings illustrate the effectiveness of our proposed method. The model had an amazing training accuracy of 96.00% and a validation accuracy of 97.00%. These high accuracy rates demonstrate the promise of our deep learning-based skin cancer prediction system as a trustworthy tool for early diagnosis, allowing healthcare practitioners to make more informed decisions and improve patient outcomes. Our effort contributes to the field of dermatological research and machine learning, providing valuable insights into the use of deep learning algorithms for skin cancer prediction. Furthermore, the publicly available HAM10000 dataset, which includes a wide range of dermatoscopic pictures, can be a great resource for academic study and future advances in the field of medical image processing and classification. Key Words– benign, CNN, early detection, skin cancer, machine learning

Early detection of melanoma skin cancer: A hybrid approach using fuzzy C-means clustering and differential evolution-based convolutional neural network

Skin cancer is a prevalent type of disease that is challenging to predict, and early detection is crucial for successful treatment. In this study, we propose an improved strategy for early detection of three types of skin cancers using medical imaging. Our approach uses fuzzy C-means clustering for image segmentation, along with various filters and image features including Local Binary Pattern (LBP), RGB color-space, and GLCM methods. We also employ a Convolutional neural network (CNN) trained with differential evolution (DE) algorithm for classification. We evaluate the proposed technique using skin cancer image datasets HAM10000, and demonstrate its superior performance compared to traditional classifiers. Our approach achieves a detection accuracy of 91 %, which is significantly higher than other traditional methods in the same domain. To enhance the accuracy of skin cancer detection in medical imaging, the proposed technique can be automated using electronic devices like mobile phones.

Applying Digital Image Processing and Machine Learning Techniques for Screening of Skin Lesions

Introduction: The presence of pigmented skin lesions is a significant global concern in the prevention of skin cancer. Detecting skin cancer at an early stage is essential for proper management and effective treatment. This study aimed to combine image processing and data mining to develop an intelligent model to screen skin cancer from skin lesions.Material and Methods: The images were taken in a clinic by smartphone. Patients over 40 years of age participated in the study. During the segmentation phase, the lesions were separated from the original images through machine vision techniques. Various features such as symmetry, border irregularity, color variation, and diameter were extracted from the images, while some features were also obtained through face-to-face examination. Finally, a neural network was employed to classify whether the lesion was cancerous or non-cancerous. In addition, MATLAB version 2022 was considered to design the model.Results: The study results indicated excellent segmentation. Using a neural network-based model, skin lesions were classified with a high level of accuracy, with 98.4% accuracy and 97% sensitivity. The results indicated the designed model significantly screened skin cancer with high accuracy.Conclusion: This model can help patients to manage self-care and become aware of their skin lesions before consulting a physician.

Skin Cancer Image Segmentation Based on Midpoint Analysis Approach.

Skin cancer affects people of all ages and is a common disease. The death toll from skin cancer rises with a late diagnosis. An automated mechanism for early-stage skin cancer detection is required to diminish the mortality rate. Visual examination with scanning or imaging screening is a common mechanism for detecting this disease, but due to its similarity to other diseases, this mechanism shows the least accuracy. This article introduces an innovative segmentation mechanism that operates on the ISIC dataset to divide skin images into critical and non-critical sections. The main objective of the research is to segment lesions from dermoscopic skin images. The suggested framework is completed in two steps. The first step is to pre-process the image; for this, we have applied a bottom hat filter for hair removal and image enhancement by applying DCT and color coefficient. In the next phase, a background subtraction method with midpoint analysis is applied for segmentation to extract the region of interest and achieves an accuracy of 95.30%. The ground truth for the validation of segmentation is accomplished by comparing the segmented images with validation data provided with the ISIC dataset.

SCDC-Net: Detection and Classification of Skin Cancer using Deep q Neural Network with Hybrid Features

The increment in radiation in atmosphere causing wide spread of skin cancer disease around the globe. Cancer of the skin is one of the most lethal forms of the illness, and it is responsible for millions of deaths annually all over the world. Because of this, the early identification of skin cancer using computer-aided diagnostic (CAD) technology might potentially save a person's life. However, skin cancer classification of performance is affected by hair artifacts and improper segmentation of lesion. The conventional methods were failed to achieve higher performance due to improper feature analysis. Therefore, to achieve the robust performance, this work was developed the transfer learning-based skin cancer detection and classification (SCDC-Net). Initially, UG-Net model was introduced to eliminate hair from skin lesions by enhancing the region of interest, which is developed by combining U-shaped deep learning network (U-Net) with generative adversarial networks (GAN). In addition, Hybrid U-Net (HU-Net) perform the segmentation operation for identifying the disease effected region of interest. Further, the features are extracted using gray level cooccurrence matrix (GLCM) matrix and discrete wavelet transform (DWT), which extracts the color, texture, statistical features. Finally, these combined characteristics are used to train a deep q neural network (DQNN) model, which then classifies a number of different skin cancer illness categories. The simulations conducted on real-time ISIC-2019 dataset proved that proposed SCDC-Net outperformed in all performance measures as compared to existing methods.

A deep learning approach for Direct Immunofluorescence pattern recognition of Autoimmune Bullous Diseases.

Artificial intelligence (AI) is reshaping healthcare, using machine and deep learning to enhance disease management. Dermatology has seen improved diagnostics, particularly in skin cancer detection, through the integration of AI. However, the potential of AI in automating immunofluorescence imaging for autoimmune bullous skin diseases remains untapped. While direct immunofluorescence (DIF) supports diagnosis, its manual interpretation can hinder efficiency. The use of deep learning to automatically classify DIF patterns, including the Intercellular Pattern (ICP) and the Linear Pattern (LP), holds promise for improving the diagnosis of autoimmune bullous skin diseases. The objectives of this study are to develop AI algorithms for automated classification of autoimmune bullous skin disease DIF patterns, such as ICP and LP. This aims to enhance diagnostic accuracy, streamline disease management, and improve patient outcomes through deep learning-driven immunofluorescence interpretation. We collected immunofluorescence images from skin biopsies of patients suspected of AIBD between January 2022 and January 2024. Skin tissue was obtained via 5-mm punch biopsy, prepared for direct immunofluorescence. Experienced dermatologists classified the images into three classes: ICP, LP, and negative. To evaluate our deep learning approach, we divided the images into training (436) and test sets (93). We employed transfer learning with pre-trained deep neural networks and conducted 5-fold cross-validation to assess model performance. Our dataset's class imbalance was addressed using weighted loss and data augmentation strategies. The models were trained for 50 epochs using Pytorch, achieving an image size of 224x224 for both CNNs and the Swin Transformer. Our study compared six CNNs and the Swin transformer for AIBDs image classification, with the Swin transformer achieving the highest average validation accuracy of 98.5%. On a separate test set, the best model attained an accuracy of 94.6%, demonstrating 95.3% sensitivity and 97.5% specificity across AIBDs classes. Visualization with Grad-CAM highlighted the model's reliance on characteristic patterns for accurate classification. The study highlighted CNN's accuracy in identifying DIF features. This approach aids automated analysis and reporting, offering reproducibility, speed, data handling, and cost-efficiency. Integrating deep learning in skin immunofluorescence promises precise diagnostics and streamlined reporting in this branch of dermatology.

Investigating Skin Cancer Diagnosis Using a Webcam-based Microcontroller System

Skin cancer can spread fast to nearby tissue and other parts of the human body if it's not diagnosed early. Most are curable only if skin cancer is found and treated in the early stages. Therefore, it's essential to seek a casual way of early diagnosis. This paper assesses a prototype system for skin cancer detection using an Arduino with an ArduCam Mega 5MP, benchmarked against smartphone diagnosis. Bandpass filters capture skin images at red (650 nm), green (532 nm), and blue (450 nm) wavelengths, measuring reflectance values. The approach aims to quantitatively determine skin melanin, oxyhemoglobin, and deoxyhemoglobin levels, aiding in various skin lesions' diagnosis. Evaluation involves comparing pixel reflectance values of images taken by smartphones and the prototype using a 3D mesh grid. Applying the modified Lambert-Beer law to reflectance values of moles, pimples, scars, scabs, and traces predicts relative levels of skin components. The system shows an 87% match with the smartphone standard, demonstrating high reliability. Further study might be needed to clarify the confirmation with clinical cases.

Optimization of Skin Cancer Detection to Improve Accuracy with the Application of Efficient Convolutional Neural Network and EfficientNetB2 Models

Skin cancer, related to ultraviolet light exposure, can be caused by genetic mutations in skin cells. Risk factors include family history, fair skin, moles, weak immunity and solar keratosis. Diagnosis involves skin examination and biopsy. Previous studies using Convolutional Neural Network (CNN) successfully classified skin cancer with an accuracy of up to 99%. The process of developing an image classification model involves structured steps. The dataset is divided into training, validation, testing. Data augmentation is performed with ImageDataGenerator to enrich the dataset. CNN model (EfficientNetB2) was customized, trained for 50 epochs. Evaluation of testing data model, metrics such as loss, accuracy, precision, recall, and F1-score. Visualization of Classification Reports, Confusion Matrix. The entire process ensured in-depth analysis of the model's performance and focused attention on prediction. The study used Kaggle's secondary data "HAM10000 Preprocessed Data" with 11644 data and three attributes, showing variations before normalization. The CNN model peaked at the 8th epoch with 86% accuracy, but there is a risk of overfitting. Evaluation using Classification Report and Confusion Matrix provides details of model performance on each skin cancer class, supporting diagnosis and treatment.This article highlights the positive impact of using the EfficientNetB2 Model on skin cancer detection through an efficient Convolutional Neural Network (CNN). The model is optimal in size and structure, resulting in superior feature representation. Evaluations including Classification Report and Confusion Matrix demonstrate the model's superiority in classifying skin cancer types, particularly in the ''akiec'' and ''bcc'' classes, with high accuracy, significantly improving detection performance.

Early detection of skin cancer using AI: Deciphering dermatology images for melanoma detection

This Review explores the transformative impact of artificial intelligence (AI) on the early detection of skin cancer, with a specific focus on melanoma, a potentially lethal form of the disease. Beginning with an overview of traditional diagnostic methods and their limitations, this paper delves into the evolution of AI within dermatology, emphasizing its application in image analysis and pattern recognition. A comprehensive examination of AI algorithms for melanoma detection, including machine learning and deep learning models, is provided. This Review critically assesses the performance metrics, training datasets, and comparative analyses with traditional methods. Addressing challenges such as data quality, interpretability, and ethical considerations, this paper outlines future directions, emphasizing ongoing research, algorithm improvements, and integration with clinical practices. Case studies and success stories highlight the real-world impact of AI in dermatology. This Review concludes by summarizing key findings and underlining the pivotal role of AI in revolutionizing early melanoma detection, with implications for personalized medicine and enhanced patient outcomes.

Application of Deep Learning with ResNet50 for Early Detection of Melanoma Skin Cancer

Cancer is a type of disease that can be fatal. Some of the cancers with the highest death rates in Indonesia include uterine cancer, breast cancer and skin cancer. The most malignant types of skin cancer are melanoma, which has a high mortality rate, especially if not detected in the early stages, and non-melanoma skin cancer (NMS Cs). Management of this disease depends on whether the type of skin cancer is malignant (malignant) or non-malignant (benign). Therefore, we need a system that can classify types of skin cancer with high accuracy. In this research, the author will use deep learning with the InceptionV3 and ResNet50 algorithms to carry out classification. The aim of this research is to classify types of skin cancer using the InceptionV3 and ResNet50 architecture. The skin cancer dataset used consists of two classes, namely Benign and Malignant, with a total of 3297 data, consisting of 660 data for testing and 2637 data for training. Research stages include data acquisition, preprocessing, classification, and analysis of results. Experimental results show that ResNet-50 produces the best performance with an accuracy level of 0.87. Innovations from this research include using a larger dataset, testing two deep learning architectures, modifying hyperparameters, and using a different layer architecture, which produces better accuracy than previous research. It is hoped that the results of this research can be applied to classify skin cancer more accurately.

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.

Ensemble Deep Learning Methods for Detecting Skin Cancer

Skin cancer is a common and possibly fatal condition. Effective treatment results are greatly influenced by early identification. Deep learning (DP) algorithms have demonstrated encouraging outcomes in skin cancer detection computer-aided diagnostic systems. This article investigates the many forms of skin cancer, such as melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), and offers a system for detecting skin cancer utilizing convolutional neural network (CNN) approaches, particularly the multi-model ResNet (M-ResNet) architecture. We present a ResNet architecture that is capable of handling deep networks and has increased skin cancer detection performance. The proposed approach uses a thorough pipeline to find skin cancer. The dataset first goes through pre-processing (PP) procedures, such as picture resizing, normalization, and augmentation approaches, to improve the model's capacity for generalization. The multi-model assembles, leading to improved accuracy, sensitivity, and specificity in skin cancer LEARNING Classification SYSTEM (SC-LCS) tasks. In this study FINAL highlights, the effectiveness of deep learning (DL)techniques, specifically the multi-model ResNet architecture, AND skin cancer LEARNING classification SYSTEM (SC-LCS) for skin cancer detection. The suggested framework seems to have promising results in accurately identifying different types of skin cancer, assisting in diagnosis and therapy at an early stage. Further research and development in this field can potentially contribute to improving healthcare systems and reducing the global burden of skin cancer-related EFFECTED and DEATH RATE.

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model.

Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network-Long Short-Term Memory (RCNN-LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN-LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1-score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes. RESEARCH HIGHLIGHTS: Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.

Federated learning algorithm-based skin cancer detection

Owing to the oversight regarding training data privacy within the realm of Deep Learning (DL), there have been inadvertent data leaks containing personal information, resulting in consequential impacts on data providers. Consequently, safeguarding data privacy throughout the deep learning process emerges as a paramount concern. In this paper, the author suggests the integration of FedAvg into the training procedure as a measure to ensure data security and privacy. In the experiments, the author first applied data augmentation to equalize the various samples in the dataset, then simulated four users using a Central Processing Unit (CPU) with four cores and established a network architecture starting with DenseNet201. Each user cloned all parameters of global model and received an equal portion of the dataset. After updating the parameters locally, the weights were aggregated by averaging and passed back to the global model. Additionally, the author introduced learning rate annealer to help the model converge better. The experimental results demonstrate that incorporating FedAvg indeed saves training time and achieves excellent performance in skin cancer classification. Despite a slight loss in accuracy, the algorithm can address privacy concerns, making the use of FedAvg highly valuable.