A Comprehensive Survey of Convolutional Neural Networks for Skin Cancer Classification and Prediction

Skin cancer, a prevalent and potentially fatal condition, requires early detection and precise classification to ensure effective treatment. In recent years, there has been a significant rise in the popularity of Convolutional Neural Networks (CNNs) prominence as a robust solution for image processing and analysis, significantly surpassing conventional techniques in skin cancer prediction and classification. This survey paper offers a thorough examination of CNNs and their diverse applications in diagnosing skin cancer, emphasizing their benefits, existing obstacles, and potential avenues for future research

DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis

Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of convolutional neural networks (CNNs) in most computer vision tasks has brought about a great leap forward in terms of performance. Nevertheless, with this performance leap, the CNN-based computer-aided diagnosis (CAD) systems have also brought a notable reduction of the useful insights provided by hand-crafted features. This paper presents DermaKNet, a CAD system based on CNNs that incorporates specific subsystems modeling properties of skin lesions that are of special interest to dermatologists aiming to improve the interpretability of its diagnosis. Our results prove that the incorporation of these subsystems not only improves the performance, but also enhances the diagnosis by providing more interpretable outputs.This work was supported in part by the National Grant TEC2014-53390-P and National Grant TEC2014-61729-EXP of the Spanish Ministry of Economy and Competitiveness, and in part by NVIDIA Corporation with the donation of the TITAN X GPUPublicad

Artificial Intelligence in Skin Cancer: A Literature Review from Diagnosis to Prevention and Beyond

Artificial Intelligence (AI) in medicine is quickly expanding, offering significant potential benefits in diagnosis and prognostication. While concerns may exist regarding its implementation, it is important for dermatologists and dermatopathologists to collaborate with technical specialists to embrace AI as a tool for enhancing medical decision-making and improving healthcare accessibility. This is particularly relevant in melanocytic neoplasms, which continue to present challenges despite years of experience. Dermatology, with its extensive medical data and images, provides an ideal field for training AI algorithms to enhance patient care. Collaborative efforts between medical professionals and technical specialists are crucial in harnessing the power of AI while ensuring it complements and enhances the existing healthcare framework. By staying informed about AI concepts and ongoing research, dermatologists can remain at the forefront of this emerging field and leverage its potential to improve patient outcomes. In conclusion, AI holds great promise in dermatology, especially in the management and analysis of Skin cancer (SC). In this review we strive to introduce the concepts of AI and its association with dermatology, providing an overview of recent studies in the field, such as existing applications and future potential in dermatology

Measles Rash Identification Using Residual Deep Convolutional Neural Network

Measles is extremely contagious and is one of the leading causes of vaccine-preventable illness and death in developing countries, claiming more than 100,000 lives each year. Measles was declared eliminated in the US in 2000 due to decades of successful vaccination for the measles. As a result, an increasing number of US healthcare professionals and the public have never seen the disease. Unfortunately, the Measles resurged in the US in 2019 with 1,282 confirmed cases. To assist in diagnosing measles, we collected more than 1300 images of a variety of skin conditions, with which we employed residual deep convolutional neural network to distinguish measles rash from other skin conditions, in an aim to create a phone application in the future. On our image dataset, our model reaches a classification accuracy of 95.2%, sensitivity of 81.7%, and specificity of 97.1%, indicating the model is effective in facilitating an accurate detection of measles to help contain measles outbreaks

Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition

Deep learning techniques have shown their superior performance in dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty of incorporating the useful dermatologist clinical knowledge into the learning process. In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically, to exploit the knowledge of morphological expressions of the lesion region and also the periphery region for melanoma identification, a lesion-based pooling and shape extraction (LPSE) scheme is designed, which transfers the structure information obtained from skin lesion segmentation into melanoma recognition. Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma recognition to skin lesion segmentation, an effective diagnosis guided feature fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual learning mechanism that further promotes the inter-task cooperation, and thus iteratively improves the joint learning capability of the model for both skin lesion segmentation and melanoma recognition. Experimental results on two publicly available skin lesion datasets show the effectiveness of the proposed method for melanoma analysis.Comment: Pattern Recognitio

Exploring the Potential of Convolutional Neural Networks in Healthcare Engineering for Skin Disease Identification

Skin disorders affect millions of individuals worldwide, underscoring the urgency of swift and accurate detection for optimal treatment outcomes. Convolutional Neural Networks (CNNs) have emerged as valuable assets for automating the identification of skin ailments. This paper conducts an exhaustive examination of the latest advancements in CNN-driven skin condition detection. Within dermatological applications, CNNs proficiently analyze intricate visual motifs and extricate distinctive features from skin imaging datasets. By undergoing training on extensive data repositories, CNNs proficiently classify an array of skin maladies such as melanoma, psoriasis, eczema, and acne. The paper spotlights pivotal progressions in CNN-centered skin ailment diagnosis, encompassing diverse CNN architectures, refinement methodologies, and data augmentation tactics. Moreover, the integration of transfer learning and ensemble approaches has further amplified the efficacy of CNN models. Despite their substantial potential, there exist pertinent challenges. The comprehensive portrayal of skin afflictions and the mitigation of biases mandate access to extensive and varied data pools. The quest for comprehending the decision-making processes propelling CNN models remains an ongoing endeavor. Ethical quandaries like algorithmic predisposition and data privacy also warrant significant consideration. By meticulously scrutinizing the evolutions, obstacles, and potential of CNN-oriented skin disorder diagnosis, this critique provides invaluable insights to researchers and medical professionals. It underscores the importance of precise and efficacious diagnostic instruments in ameliorating patient outcomes and curbing healthcare expenditures

A Mobile-Based Skin Disease Identification System Using Convolutional Neural Networks

Skin diseases pose significant challenges in the field of dermatology. In recent years, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image recognition and analysis tasks. This research paper presents a comprehensive study on the application of CNNs for skin disease diagnosis. We propose a CNN-based framework for skin disease diagnosis, which utilizes a large dataset of dermatological images to accurately identify various skin diseases. The proposed model leverages the deep learning capabilities of CNNs to learn discriminative features from input images, enabling accurate and efficient diagnosis. We demonstrate improved accuracy and efficiency in skin disease diagnosis by employing pre-trained models. Our proposed model enables accurate classification of skin diseases into high, medium, and low severity categories by leveraging a large dataset of annotated images, assisting healthcare professionals in prioritizing treatment strategies. In conclusion, this research paper presents a comprehensive study on the application of CNNs for skin disease diagnosis, skin lesion classification, melanoma skin cancer classification, and skin disease severity classification. The proposed models showcase significant advancements in the field of dermatology, providing accurate and efficient tools for dermatologists and healthcare professionals. The findings of this research contribute to improving the diagnosis, classification, and severity assessment of skin diseases, ultimately enhancing patient care and treatment outcomes