Evaluation of CNN-Based Approaches to Adverse Weather Image Classification for Autonomous Driving Systems by Viktoria Afxentiou, Tanya Vladimirova

“…This is followed by a comprehensive comparative performance analysis for both single-label and multi-label classification of AWCs images, which is grounded in an extensive experimental modelling effort and serves the purpose of validating the proposed novel evaluation methodology. …”
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An Iterative Pixel-Based Dimensional Voting Model for High Spatial-Resolution Image Classification by Muhamad Asyraf Mohd Pouzi, Haza Nuzly Abdull Hamed, Muhammad Razib Othman, Hishammuddin Asmuni, Mohd Adham Isa, Husni Ruslai

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Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks by Rami Haffar, David Sanchez, Josep Domingo-Ferrer

“…The MTF data set comes in two versions: a non-curated set containing 132,816 images of 640 individuals, and a manually curated set with 5,246 images of 240 individuals, meticulously selected to maximize their classification quality. …”
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A Novel Framework for Whole-Slide Pathological Image Classification Based on the Cascaded Attention Mechanism by Dehua Liu, Bin Hu

“…Automated models can rapidly and accurately process large datasets, revolutionizing tumor detection and classification. …”
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Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier by Ali Kirkbas, Aydin Kizilkaya

“…The FDM is responsible for generating time–frequency (T-F) representations of ECG recordings. The classification process is performed with feature images applied as input to the classifier model. …”
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saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification Dataset by Umi Chasanah, Gilang Putra, Sahid Bismantoko, Sofwan Hidayat, Tri Widodo, Mohammad Rosyidi

“…The primary contribution of saLFIA is a labeling tool designed to generate new datasets from public source images, focusing on vehicle classification using YOLOv3 and SSD algorithms. …”
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Deep learning classification integrating embryo images with associated clinical information from ART cycles by Mohamed Salih, Christopher Austin, Krishna Mantravadi, Eva Seow, Sutthipat Jitanantawittaya, Sandeep Reddy, Beverley Vollenhoven, Hamid Rezatofighi, Fabrizzio Horta

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Uncertainty-aware automatic TNM staging classification for [18F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learni... by Stephen H. Barlow, Sugama Chicklore, Yulan He, Sebastien Ourselin, Thomas Wagner, Anna Barnes, Gary J.R. Cook

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Single Image Super-Resolution Enhancement using Luminance Map and Atmospheric Light Removal by Samira Poormajidi, Mohammad Shayegan

“…Super resolution algorithms attempt to reconstruct high resolution images from low resolution images and it can be considered as a preprocessing step for object recognition and image classification. …”
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Ensemble Learning-Based Alzheimer’s Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images by Young Jae Huh, Jun-ha Park, Young Jae Kim, Kwang Gi Kim

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Multimodal Ensemble Fusion Deep Learning Using Histopathological Images and Clinical Data for Glioma Subtype Classification by Satoshi Shirae, Shyam Sundar Debsarkar, Hiroharu Kawanaka, Bruce Aronow, V. B. Surya Prasath

“…Our proposed ensemble fusion approach significantly outperforms the classification using only histopathology images alone with deep learning models. …”
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Explainable multi-view transformer framework with mutual learning for precision breast cancer pathology image classification by Haewon Byeon, Mahmood Alsaadi, Richa Vijay, Purshottam J. Assudani, Ashit Kumar Dutta, Monika Bansal, Pavitar Parkash Singh, Mukesh Soni, Mohammed Wasim Bhatt

“…To overcome these challenges and contribute to the advancement of Explainable AI (XAI) in precision cancer diagnosis, this paper proposes MVT-OFML (Multi-View Transformer Online Fusion Mutual Learning), a novel and interpretable classification framework for breast cancer pathology images. …”
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FPGA-accelerated SpeckleNN with SNL for real-time X-ray single-particle imaging by Abhilasha Dave, Cong Wang, James Russell, Ryan Herbst, Jana Thayer

“…We present the implementation of a specialized version of our previously published unified embedding model, SpeckleNN, for real-time speckle pattern classification in X-ray Single-Particle Imaging (SPI), using the SLAC Neural Network Library (SNL) on an FPGA platform. …”
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Enhancing neuromolecular imaging classification in low-data regimes with generative machine learning: A case study in HDAC PET/MR imaging of alcohol use disorder by Tyler N. Meyer, Olga Andreeva, Roger D. Weiss, Wei Ding, Iris Shen, Changning Wang, Ping Chen, Tewodros Mulugeta Dagnew

“…Our primary objective is to enhance single-subject classification of neuromolecular imaging data and facilitate biomarker discovery. …”
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Crushed Stone Grain Shapes Classification Using Convolutional Neural Networks by Alexey N. Beskopylny, Evgenii M. Shcherban’, Sergey A. Stel’makh, Irina Razveeva, Alexander L. Mailyan, Diana Elshaeva, Andrei Chernil’nik, Nadezhda I. Nikora, Gleb Onore

“…Three-dimensional images of acicular, lamellar, and cuboid grains were converted into single-channel digital tensor format. …”
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SSL-MAE: Adaptive Semisupervised Learning Framework for Multilabel Classification of Remote Sensing Images Using Masked Autoencoders by Marjan Stoimchev, Jurica Levatic, Dragi Kocev, Saso Dzeroski

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Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images by A. A. Abd El-Aziz, Mahmood A. Mahmood, Sameh Abd El-Ghany

“…Digital image processing (DIP) and deep learning (DL) algorithms can be employed to analyze the HIs of five different types of lung and colon tissues. …”
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Klasifikasi Penyakit pada Tanaman Berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network by Denis Aji Pangestu, Okta Qomaruddin Aziz, Cahyo Crysdian

“…Plant disease identification typically requires experienced experts, but this process is time-consuming and costly. This research aims to develop a plant disease classification model using Convolutional Neural Network (CNN) to assist farmers in identifying diseases in rice, corn, tomato, and potato plants based on leaf images. …”
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Plant Stress Detection Using a Three-Dimensional Analysis from a Single RGB Image by Madaín Pérez-Patricio, J. A. de Jesús Osuna-Coutiño, German Ríos-Toledo, Abiel Aguilar-González, J. L. Camas-Anzueto, N. A. Morales-Navarro, J. Renán Velázquez-González, Luis Ángel Cundapí-López

“…Plant stress detection involves the process of Identification, Classification, Quantification, and Prediction (ICQP) in crop stress. …”
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Wheat Powdery Mildew Severity Classification Based on an Improved ResNet34 Model by Meilin Li, Yufeng Guo, Wei Guo, Hongbo Qiao, Lei Shi, Yang Liu, Guang Zheng, Hui Zhang, Qiang Wang

“…In this study, we present QY-SE-MResNet34, a deep learning-based classification model that builds upon ResNet34 to perform multi-class classification of wheat leaf images and assess powdery mildew severity at the single-leaf level. …”
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