Automatic image segmentation using Region-Based convolutional networks for Melanoma skin cancer detection by Karen Dayana Tovar-Parra, Luis Alexander Calvo-Valverde, Ernesto Montero-Zeledón, Mac Arturo Murillo-Fernández, Jose Esteban Perez-Hidalgo, Dionisio Alberto Gutiérrez-Fallas

“…This research deals with targeting most of the common Melanoma stains or spots that could potentially evolve to Melanoma skin cancer. Region-based Convolutional Neural Networks were used as the model to detect and segment images of the skin area of interest. …”
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Automatic melanoma detection using an optimized five-stream convolutional neural network by Vida Esmaeili, Mahmood Mohassel Feghhi, Hadi Seyedarabi

“…For this reason, these convolutional neural networks (CNNs) cannot perceive the changed or rotated image samples as similar. …”
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Segmentation of dermatoscopic images of skin lesions. Comparison of methods by A. F. Smalyuk, M. S. Dzeshka, I. D. Kupchykava

“…This will allow to identify changes that have occurred in skin lesions over time, for a more accurate diagnosis of their malignancy. …”
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Skin lesion segmentation with a multiscale input fusion U-Net incorporating Res2-SE and pyramid dilated convolution by Zhihui Liu, Jie Hu, Xulu Gong, Fuzhong Li

“…Accurate segmentation aids in identifying and localizing diseases, monitoring morphological changes, and extracting features for further diagnosis, especially in the early detection of skin cancer. …”
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Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning by Teng-Li Lin, Arvind Mukundan, Riya Karmakar, Praveen Avala, Wen-Yen Chang, Hsiang-Chen Wang

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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…This review paper explores the state-of-the-art developments in machine learning methods designed especially for skin disease identification. Investigate the effectiveness and performance of several algorithms, such as the flexible k-nearest neighbor, the sturdy support vector machine (SVM), and the complex convolutional neural networks (CNNs), advanced techniques for automated skin disease detection encompass deep learning methods such as recurrent neural networks (RNNs) for sequential data processing, generative adversarial networks (GANs) for generating synthetic data, and attention mechanisms for focusing on relevant image regions by means of a thorough examination of the most recent studies. …”
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Skincare Recommendation System Based on Facial Skin Type with Real-Time Weather Integration by Gabrielle Sheila Sylvagno, Theresia Herlina Rochadiani

“…Skin conditions can be significantly affected by unpredictable weather changes, creating the need for a solution that can provide personalized skincare product recommendations. …”
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Efficient Tactile Perception in Robotics: Reducing Data Redundancy through Compression and Normalization in Spiking Graph Convolutional Networks by Elahe Rezaee Ahvanooii, Sheis Abolmaali

“…Recent developments in electronic skin technology have brought robots closer to achieving human-like tactile perception by enabling asynchronous responses to temperature and pressure changes, thereby enhancing robotic precision in tasks like object manipulation and grasping. …”
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Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food by Sreevidya R. C., Jalaja G, Sajitha N, D. Lakshmi Padmaja, S. Nagaprasad, Kumud Pant, Yekula Prasanna Kumar

“…The study’s purpose is to comprehend the vital function performed by DL methods in supporting healthcare professionals in easier skin cancer detection using big data networks. The present research analyzes the accuracy, sensitivity, and specificity of “Convolutional Neural Network” (CNN) for DL in the early detection of skin cancer. …”
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Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration by Maridu Bhargavi, Sivadi Balakrishna

“…To address these issues, this research proposes the DRMv2Net model, a feature fusion deep learning-based technique that integrates multiple pre-trained convolutional neural networks to enhance skin cancer diagnosis. …”
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Applications and prospects of artificial intelligence in the auxiliary diagnosis of nail diseases by HE Mengwen, MAI Sien, MA Han

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Dynamic Gesture Recognition and Interaction of Monocular Camera Based on Deep Learning by SUNBo wen, YU Feng

“…In the aspect of gesture interaction, the motion on the plane of gesture image is judged by the change of gesture center of gravity, and the displacement is detected to prevent the sudden change of gesture image position caused by the interference. …”
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Sign language detection dataset: A resource for AI-based recognition systemsMendeley Data by Bindu Garg, Manisha Kasar, Priyanka Paygude, Amol Dhumane, Srinivas Ambala, Jitendra Rajpurohit, Abhay Sharma, Vidula Meshram, Amber Vats, Achyut Kashyap

“…Hand gestures are classified by the Convolutional Neural Network (CNN) model to different signs. …”
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AI-assisted Total Body Dermoscopic Evaluation of Changes in Melanocytic Nevi during Pregnancy: A Prospective, Comparative Study of 2,799 Nevi by Jelissa K. Peter, Fabrice Helfenstein, Sara E. Cerminara, Julia-Tatjana Maul, Mara L. Zehnder, Dagmar Jamiolkowski, Elisabeth Roider, Beda Mühleisen, Irene Hösli, Alexander A. Navarini, Lara V. Maul

“…We conducted the first prospective study to investigate dermoscopic changes in MN comparing pregnant with non-pregnant women on all body parts using a market-approved convolutional neural network (CNN). …”
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NAILS: Normalized Artificial Intelligence Labeling Sensor for Self-Care Health by Livio Tenze, Enrique Canessa

“…The NAILS leverages a pre-trained Convolutional Neural Network model to segment and label fingernail regions from fingernail images, normalizing RGB values to monitor tiny color changes via a GUI and the use of an HD webcam in real time. …”
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Impact of Safety Signage Placement on Evacuation Behavior in Virtual Fire Scenarios Based on EDA Data by Wenqi Song, Xu Feng, Yu Song

“…Five features are extracted from the EDA signal: PhasicData, PhasicDriver, Skin Conductance (SC), TonicData, and TonicDriver. …”
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A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion by Jaleh Farmani, Ghazal Bargshady, Stefanos Gkikas, Manolis Tsiknakis, Raul Fernandez Rojas

“…Physiological indicators offer valuable insights into pain-related states and are generally less influenced by individual variability compared to behavioural modalities, such as facial expressions. Skin conductance, regulated by sweat gland activity, and the heart’s electrical signals are both influenced by changes in the sympathetic nervous system. …”
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Will Artificial Intelligence Replace Physicians or Augment Their Capabilities? by Sara Rahmati Roodsari, Alireza Zali, Mohammad Rahmati-Roodsari, Behina Forouzanmehr

“…In ophthalmology, convolutional and deep learning have made it possible to quickly and non-invasively interpret the retina. …”
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Harnessing Multiple Level Features to Improve Segmentation Performance of Deep Neural Network: A Case Study in Magnetic Resonance Imaging of Nasopharyngeal Cancer by Rongzhi Mao, Liangxu Xie, Xiaohua Lu, Jialu Pei, Xiaojun Xu, Shan Chang

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