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Skin cancer is one of the most life-threatening diseases caused by the abnormal growth of the skin cells, when exposed to ultraviolet radiation. Early detection seems to be more crucial for reducing aberrant cell proliferation because the mortality rate is rapidly rising. Although multiple researches are available based on the skin cancer detection...
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Being the most deadly kind of skin cancer, melanoma presents a serious risk to life and is increasingly prevalent, particularly among men. It is characterized by aggressive cell multiplication that can spread within the body if left untreated, making early detection crucial for successful treatment. While machine learning techniques have been used...
Citations
... [26,27]. Kumar et al. proposed novel skin cancer detection algorithm using deep CNN [28][29][30] Almeida et al. employed (CNNs) to address the task of reaction cataloguing. Recurrent Neural Networks (RNNs) demonstrate effectiveness in processing sequential data, making them frequently utilized NLP [31]. ...
An essential function of natural language processing is sentiment analysis. Which holds substantial significance in understanding public opinion across diverse domains. However, while sentiment analysis methodologies abound in English, there exists a notable scarcity of research addressing sentiment analysis in languages like Hindi. In response, the above paper provides a pioneering aspect to Hindi sentiment analysis through the development of a hybrid deep learning-machine learning model integrated with a metaheuristic optimization algorithm. By amalgamating the strengths for normal machine learning (ML) techniques and deep learning (DL), this model endeavours to boost accuracy and robustness in sentiment classification tasks specific to Hindi text. Furthermore, the inclusion of a metaheuristic optimization algorithm aims to optimize crucial model parameters, thereby improving convergence speed and overall performance. The proposed approach is motivated by the need for more comprehensive sentiment analysis techniques tailored for multilingual social media data, particularly in languages like Hindi, which are prevalent on various online platforms. Through empirical evaluation and comparative analysis, this paper demonstrates the efficacy and potential applications of the proposed hybrid model in real-world sentiment analysis scenarios. This research contributes to bridging the gap in sentiment analysis research for non-English languages and lays the foundation for further advancements in multilingual sentiment analysis methodologies.
... In general, diabetes is known as a metabolic condition categorized by increased levels of blood glucose and also affects the organs of the body, such as blood vessels and nerves 5 . If untreated, it leads to cardiovascular disease, nerve damage, and kidney failure 6 . Likewise, a neurological impairment led by a blockage and disturbance in the blood supply to the brain part is known as a stroke 7 . ...
Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, and continuous monitoring and early detection are necessary for effective management. The Metaverse Environment (ME) has gained attention in the digital healthcare environment; yet, it lacks adequate support for disabled individuals, including deaf and dumb people, and also faces challenges in security, generalizability, and feature selection. To overcome these limitations, a novel probabilistic-centric optimized recurrent sechelliott neural network (PO-RSNN)-based diabetes prediction (DP) and Fuzzy Z-log-clipping inference system (FZCIS)-based severity level estimation in ME is carried out. The proposed system integrates Montwisted-Jaco curve cryptography (MJCC) for secured data transmission, Aransign-principal component analysis (A-PCA) for feature dimensionality reduction, and synthetic minority oversampling technique (SMOTE) to address data imbalance. The diagnosed results are securely stored in the BlockChain (BC) for enhanced privacy and traceability. The experimental validation demonstrated the superior performance of the proposed system by achieving 98.97% accuracy in DP and 98.89% accuracy in stroke analysis, outperforming existing classifiers. Also, the proposed MJCC technique attained 98.92% efficiency, surpassing the traditional encryption models. Thus, the proposed system produces a secure, scalable, and highly accurate DP and stroke analysis in ME. Further, the research will extend the approach to other CD like cancer and heart disease to improve the predictive performance.
... Skin cancer is recognized as one of the most prevalent cancers worldwide and a significant cause of mortality [1]. It primarily develops due to prolonged exposure to ultraviolet (UV) radiation from the sun [2], which leads to the formation of tumors. Other contributing factors include air pollution and unhealthy lifestyles [3]. ...
The rapid advancements in artificial intelligence (AI) have significantly impacted modern healthcare, particularly for skin cancer detection in the field of dermatology. Skin cancer has become a significant public health challenge, highlighting the importance of early detection to improve patient outcomes. AI models have revolutionized skin cancer diagnosis by enhancing the accuracy and efficiency of detecting malignancies in the early stages. This review provides a comprehensive comparison of the latest AI technologies used in skin cancer detection, including segmentation, classification, and reinforcement learning models. It also explores the integration of transformer and multimodal technologies, which have further refined the diagnostic process. Despite the remarkable progress, challenges remain in the practical deployment of these AI models, particularly those concerning data diversity, model interpretability, and clinical integration. Future research directions should focus on overcoming these limitations to fully harness AI’s potential in dermatology, aiming to improve diagnostic accuracy, reduce healthcare costs, and ultimately save lives.
The increasing incidence of and resulting deaths associated with malignant skin tumors are a public health problem that can be minimized if detection strategies are improved. Currently, diagnosis is heavily based on physicians’ judgment and experience, which can occasionally lead to the worsening of the lesion or needless biopsies. Several non-invasive imaging modalities, e.g., confocal scanning laser microscopy or multiphoton laser scanning microscopy, have been explored for skin cancer assessment, which have been aligned with different artificial intelligence (AI) strategies to assist in the diagnostic task, based on several image features, thus making the process more reliable and faster. This systematic review concerns the implementation of AI methods for skin tumor classification with different imaging modalities, following the PRISMA guidelines. In total, 206 records were retrieved and qualitatively analyzed. Diagnostic potential was found for several techniques, particularly for dermoscopy images, with strategies yielding classification results close to perfection. Learning approaches based on support vector machines and artificial neural networks seem to be preferred, with a recent focus on convolutional neural networks. Still, detailed descriptions of training/testing conditions are lacking in some reports, hampering reproduction. The use of AI methods in skin cancer diagnosis is an expanding field, with future work aiming to construct optimal learning approaches and strategies. Ultimately, early detection could be optimized, improving patient outcomes, even in areas where healthcare is scarce.
