Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using deep learning approach as machine vision tool can overcome some challenges. This research proposes an automated classifier based on convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN carefully designed by organizing many layers that are responsible for extracting low high-level features images in unique fashion. Other vital...

The most deadly type of skin cancer is melanoma. A visual examination does not provide an accurate diagnosis melanoma during its early to middle stages. Therefore, automated model could be developed that assists with detection. It possible limit the severity by detecting it and treating promptly. This study aims develop efficient approaches for various phases computer-aided (CAD), such as preprocessing, segmentation, classification. first step CAD pipeline includes proposed hybrid method,...

Abstract Background Melanoma is the most dangerous and aggressive form among skin cancers, exhibiting a high mortality rate worldwide. Biopsy histopathological analysis are standard procedures for cancer detection prevention in clinical settings. A significant step diagnosis process deep understanding of patterns, size, color, structure lesions based on images obtained through dermatoscopes infected area. However, manual segmentation lesion region time-consuming because evolves changes its...

Abstract One fatal kind of cancer that can be successfully treated if detected early is Melanoma skin cancer. In this paper, an embedded diagnostic device to aid in the identification proposed. The reconfigurable computing advances and modern design approaches are used construct accurate efficient medical for identifying possible aim develop implement hardware software modules collaborate diagnose images by analyzing them tumors. This study employed ResNet50 deep learning model, which...

Abstract Automatic lesion segmentation is a key phase of skin analysis that significantly increases the performance subsequent classification steps. Segmentation highly complex task due to varying nature lesions, such as unique shapes, different colors, and structures. In this study, two‐step system proposed comprising preprocessing algorithm network. The hairlines removal designed using morphological operators eliminate noise artifacts. resulting output images are fed convolutional neural...

Developing a fast and accurate classifier is an important part of computer-aided diagnosis system for skin cancer. Melanoma the most dangerous form cancer which has high mortality rate. Early detection prognosis melanoma can improve survival rates. In this paper, we propose deep convolutional neural network automated that scalable to accommodate variety hardware software constraints. Dermoscopic images collected from open sources were used training network. The trained was then tested on...

Deep learning and computer vision have achieved remarkable success in many areas of machine medical diagnostics. However, there is still a gap between dermatologists' skin cancer diagnosis reliable computer-aided melanoma detection. There are several reasons behind this gap, the availability insufficient data for training deep networks one them. Data augmen-tation popular technique to increase manifolds mitigate lack data. In paper, conditional generative adversarial network (CGAN) proposed...

Abstract Background : Among skin cancers, melanoma is the most dangerous and aggressive form, exhibiting a high mortality rate worldwide. Biopsy histopatholog-ical analysis are common procedures for cancer detection prevention in clinical settings. A significant step involved diagnosis process deep understanding of patterns, size, color, structure lesions based on images obtained through dermatoscopes infected area. However, manual seg-mentation lesion region time-consuming because evolves...

Skin cancer, one of the most prevalent and life-threatening cancers globally, has become a focus deep learning applications due to its significant impact on diagnostic accuracy. This research specifically addresses lesion segmentation in skin cancer images, recognizing direct influence classification precision. Six diverse models, including DeepLabV3+, EfficientNetB7, VGG19, Attention-UNet, MultiRes-UNet, Transformer-UNet, were implemented. The effective DeepLabV3+ had their predictions...

Early skin cancer detection and its treatment are crucial for reducing death rates worldwide. Deep learning techniques have been used successfully to develop an automatic lesion system. This study explores the impact of pre-processing steps such as data augmentation, contrast enhancement, segmentation on improving convolutional neural network (CNN) performance classification. The classification was designed from scratch by uniquely organizing layers using a different number kernels, depth...

Melanoma is a dangerous skin cancer that requires early detection for successful treatment. This study presents an implantable diagnostic device points to revolutionize of melanoma. The combines the most recent designs with re-configurable computing strategies, permitting precise diagnosis and analysis potential melanoma pictures. Furthermore, it utilizes advanced hardware software components. Utilizing VGG-16 profound learning model augmented HAM10000 dataset, this accomplishes staggering...

Skin cancer represents a significant health concern, necessitating early detection as it plays role in successful treatment and improved patient outcomes. This study explores the potential of deep learning technology enhancing skin through convolutional neural network (CNN) models. It aims to develop an optimized system using three popular CNN models: Efficient-NetB4, InceptionV3, MobileNetV2. The main objectives this research involved evaluating performance these models classifying types...

Deep learning techniques have been widely employed in semantic segmentation problems, especially medical image analysis, for understanding patterns. Skin cancer is a life-threatening problem, whereas timely detection can prevent and reduce the mortality rate. The aim to segment lesion area from skin help experts process of deeply tissues cells' formation. Thus, we proposed an improved fully convolutional neural network (FCNN) architecture dermoscopic images. FCNN consists multiple feature...

Skin cancer is one of the deadliest types cancers that need to be detected at an early stage as it can widely spread affecting other organs body. Thus, our objective develop efficient mobile application dedicated detection skin in primary healthcare a low cost. In design Android-based application, EfficientNetV2 deep learning model achieved highest accuracy and feasibility deployment offline compared CNN pre-trained models. The designed system classifies three classes: healthy, benign,...

Skin lesion analysis is a tedious and challenging task, thus, in this research the suitability of employing machine learning or deep approaches for automatic segmentation on dermoscopic skin cancer images determined. The segmented region can assist clinical experts understanding complex structure internal pattern to find correct type its early diagnosis prevention. In study, I present two methodologies performing segmentation: learning-based optimized K-means with Firefly Algorithm (FA)...