Partial image encryption using format-preserving encryption in image processing systems for Internet of things environment by Wonyoung Jang, Sun-Young Lee

“…Concomitant with advances in technology, the number of systems and devices that utilize image data has increased. Nowadays, image processing devices incorporated into systems, such as the Internet of things, drones, and closed-circuit television, can collect images of people and automatically share them with networks. …”
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Image Generation and Lesion Segmentation of Brain Tumors and Stroke Based on GAN and 3D ResU-Net by Mingkang Sun, Xiang Li, Weiye Sun

“…For example, in T1<inline-formula> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> Flair conversion, the generative multi-modal image analysis model based on perceptual loop consistency had an average peak signal-to-noise ratio of <inline-formula> <tex-math notation="LaTeX">$23.951~\pm ~2.735$ </tex-math></inline-formula>, an average structural similarity of <inline-formula> <tex-math notation="LaTeX">$0.873~\pm ~0.046$ </tex-math></inline-formula>, and an average root mean square error of <inline-formula> <tex-math notation="LaTeX">$16.998~\pm ~6.184$ </tex-math></inline-formula>. …”
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Automating field‐based floral surveys with machine learning by Nicholas Sookhan, Shane Sookhan, Devlin Grewal, J. Scott MacIvor

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Towards precision agriculture tea leaf disease detection using CNNs and image processing by Irfan Sadiq Rahat, Hritwik Ghosh, Suresh Dara, Shashi Kant

“…Abstract In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task of classifying common diseases in tea leaves, leveraging advanced image analysis techniques. Our model is distinguished by its complex multi-layer architecture, crafted to adeptly handle 256 × 256 pixel images across three color channels (RGB). …”
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Deep learning-based identification of precipitation clouds from all-sky camera data for observatory safety by Mohammad H. Zhoolideh Haghighi, Alireza Ghasrimanesh, Habib Khosroshahi

“…We train our model on a set of roughly 2445 images taken by the INO all-sky camera through the deep learning method based on the EfficientNet network. …”
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Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images by Mahdi Azmoodeh-Kalati, Hasti Shabani, Mohammad Sadegh Maghareh, Zeynab Barzegar, Reza Lashgari

“…The advent of whole-slide scanners has revolutionized this process by enabling the use of Computer-Aided Detection (CAD) systems for automated analysis. In this study, we utilize state-of-the-art Convolutional Neural Networks (CNNs), specifically EfficientNetV1 and EfficientNetV2, for the binary classification of the BreakHis dataset—a collection of histopathological images categorized as benign or malignant breast tissues. …”
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Comparative Analysis of AI Models for Atypical Pigmented Facial Lesion Diagnosis by Alessandra Cartocci, Alessio Luschi, Linda Tognetti, Elisa Cinotti, Francesca Farnetani, Aimilios Lallas, John Paoli, Caterina Longo, Elvira Moscarella, Danica Tiodorovic, Ignazio Stanganelli, Mariano Suppa, Emi Dika, Iris Zalaudek, Maria Antonietta Pizzichetta, Jean Luc Perrot, Gabriele Cevenini, Ernesto Iadanza, Giovanni Rubegni, Harald Kittler, Philipp Tschandl, Pietro Rubegni

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Non-Invasive Localization of Epileptogenic Zone in Drug-Resistant Epilepsy Based on Time–Frequency Analysis and VGG Convolutional Neural Network by Yaqing Liu, Yalin Wang, Tiancheng Wang

“…Previous researchers have proposed a range of methods for this purpose, but these suffer from limits such as unclear post-operative outcomes, invasiveness, limited data volume, and single DRE type. This study constructed a non-invasive epilepsy localization method, integrating sLORETA source imaging, time–frequency analysis, and Visual Geometry Group (VGG-16) deep learning. …”
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Developing an artificial intelligence-based progressive growing GAN for high-quality facial profile generation and evaluation through turing test and aesthetic analysis by Shahab Kavousinejad, Kazem Dalaie, Mohammad Behnaz, Soodeh Tahmasbi, Asghar Ebadifar, Hoori Mirmohammadsadeghi

“…Abstract This study aimed to develop a Progressive Growing Generative Adversarial Network with Gradient Penalty (WPGGAN-GP) to generate high-quality facial profile images, addressing the scarcity of diverse training data in orthodontics. …”
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Optimal Res-UNET architecture with deep supervision for tumor segmentation by Rahman Maqsood, Fazeel Abid, Jawad Rasheed, Jawad Rasheed, Jawad Rasheed, Onur Osman, Shtwai Alsubai

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Morphological Analysis and Subtype Detection of Acute Myeloid Leukemia in High-Resolution Blood Smears Using ConvNeXT by Mubarak Taiwo Mustapha, Dilber Uzun Ozsahin

“…Automated AML subtype detection is especially important for underrepresented subtypes to ensure equitable diagnostics; (2) Methods: This study explores the potential of ConvNeXt, an advanced convolutional neural network architecture, for classifying high-resolution peripheral blood smear images into AML subtypes. …”
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FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms by Ripon Kumar Debnath, Md. Abdur Rahman, Sami Azam, Yan Zhang, Mirjam Jonkman

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A compressed image encryption algorithm leveraging optimized 3D chaotic maps for secure image communication by Akshat Tiwari, Prachi Diwan, Tarun Dhar Diwan, Mahdal Miroslav, S. P. Samal

“…This paper presents a significant advancement in the field of secure image encryption to meets the increasing demands for data security in modern digital communication networks.…”
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Impact of lens autofluorescence and opacification on retinal imaging by Frank G Holz, Maximilian Pfau, Monika Fleckenstein, Raffael Liegl, Geena C Rennen, Marc Vaisband, Jan Hasenauer

“…CNN image quality prediction was excellent (average mean absolute error (MAE) 0.9). …”
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A Novel 3D Convolutional Neural Network-Based Deep Learning Model for Spatiotemporal Feature Mapping for Video Analysis: Feasibility Study for Gastrointestinal Endoscopic Video Cla... by Mrinal Kanti Dhar, Mou Deb, Poonguzhali Elangovan, Keerthy Gopalakrishnan, Divyanshi Sood, Avneet Kaur, Charmy Parikh, Swetha Rapolu, Gianeshwaree Alias Rachna Panjwani, Rabiah Aslam Ansari, Naghmeh Asadimanesh, Shiva Sankari Karuppiah, Scott A. Helgeson, Venkata S. Akshintala, Shivaram P. Arunachalam

“…Despite advances in DL, most existing models in medical AI focus on static images, overlooking critical temporal cues present in video data. …”
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EarlyExodus: Leveraging early exits to mitigate backdoor vulnerability in deep learning by Salmane Douch, M. Riduan Abid, Khalid Zine-Dine, Driss Bouzidi, Fatima Ezzahra El Aidos, Driss Benhaddou

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Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)... by 周贯旭(ZHOU Guanxu), 姜红(JIANG Hong), 徐雪芳(XU Xuefang)

“…The performance of the model using Precision, Recall, and F1-score metrics are evaluated. Through the analysis of the 1D-CNN, 2D-CNN, and feature fusion neural network classification models established on one-dimensional spectral data and hyperspectral images, it is show that the accuracy of the three models was 89.0%, 71.6%, and 93.3%, respectively. …”
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Aortic Aneurysm Inflammatory Cell Detection with Deep Learning methods by Kristóf-Gergö Nagy, Csaba Szferle, Attila Fintha

“…INTRODUCTION: In digital pathology, neural networks such as the Multilayer Perceptron (MLP) and Residual Neural Network (ResNet) are becoming increasingly prevalent for the analysis of tissue structure. …”
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Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform by Sara Bagherzadeh, Mohammad Reza Norouzi, Amirhesam Ghasri, Pouya Tolou Kouroshi, Sepideh Bahri Hampa, Fatemeh Farokhshad, Ahmad Shalbaf

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MCAUnet: a deep learning framework for automated quantification of body composition in liver cirrhosis patients by Jiening Wang, Shuqi Xia, Jie Zhang, Xinyi Wang, Cai Zhao, Wen Zheng

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