Multimodal image fusion for ich detection and classification using parallel Dl models by Sri Sangepu Nagaraju, S. Prince Mary, V. Pavani Chandra, Nandam Gayatri

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Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification. by Saleem Mustafa, Arfan Jaffar, Muhammad Rashid, Sheeraz Akram, Sohail Masood Bhatti

“…This paper offers a cutting-edge hybrid deep learning approach of better segmentation and classification of skin lesions. The proposed method incorporates three key stages: preprocessing, segmentation of lesions, and classification of lesions. …”
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Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm by K. Gopalakrishnan, R. Sivaraj, M. Vijayakumar

“…Conventional models of weed control like extensive pesticide use, appear with the hassle of environmental pollution and advancing weed battle. …”
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Are Artificial Intelligence Models Listening Like Cardiologists? Bridging the Gap Between Artificial Intelligence and Clinical Reasoning in Heart-Sound Classification Using Explain... by Sami Alrabie, Ahmed Barnawi

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Automated road surface classification in OpenStreetMap using MaskCNN and aerial imagery by R. Parvathi, V. Pattabiraman, Nancy Saxena, Aakarsh Mishra, Utkarsh Mishra, Ansh Pandey

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Nature-Inspired Multi-Level Thresholding Integrated with CNN for Accurate COVID-19 and Lung Disease Classification in Chest X-Ray Images by Wafa Gtifa, Ayoub Mhaouch, Nasser Alsharif, Turke Althobaiti, Anis Sakly

“…This study addresses the diagnostic gap by introducing a novel hybrid framework for precise segmentation and classification of lung conditions. <b>Methods</b>: The approach combines multi-level thresholding with the advanced metaheuristic optimization algorithms animal migration optimization (AMO), electromagnetism-like optimization (EMO), and the harmony search algorithm (HSA) to enhance image segmentation. …”
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ACT-FRCNN: Progress Towards Transformer-Based Object Detection by Sukana Zulfqar, Zenab Elgamal, Muhammad Azam Zia, Abdul Razzaq, Sami Ullah, Hussain Dawood

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Evaluating segmentation methods for UAV-Based Spoil Pile Delineation by Sureka Thiruchittampalam, Bikram Pratap Banerjee, Nancy F Glenn, Simit Raval

“…However, object-based classification’s effectiveness hinges on segmentation, an aspect often overlooked in spoil pile analysis. …”
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Eustachian tube: subtemporal exposure and proposed classification by Ahmed M. Ashour, Saleem I. Abdulrauf

“…Conclusions The study proposes a classification of the ET into three segments based on its anatomical relationships during subtemporal exposure. …”
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imageseg: An R package for deep learning‐based image segmentation by Jürgen Niedballa, Jan Axtner, Timm Fabian Döbert, Andrew Tilker, An Nguyen, Seth T. Wong, Christian Fiderer, Marco Heurich, Andreas Wilting

“…While CNN‐based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. …”
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Advances and Challenges in Deep Learning for Automated Welding Defect Detection: A Technical Survey by Abdulrahim Mohammed, Muhammad Hussain

“…This paper presents a comprehensive review of state-of-the-art Deep Learning (DL) models tailored for welding defect detection, segmentation, and classification, emphasizing technical advancements and persistent challenges. …”
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Ensemble-based eye disease detection system utilizing fundus and vascular structures by Hongjie Yu, Xingbo Dong

“…This framework leverages dual-branch input, incorporating both retinal images and vessel segmentation images, along with transfer learning and ensemble learning algorithms. …”
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Semantic Co-Occurrence and Relationship Modeling for Remote Sensing Image Segmentation by Yinxing Zhang, Haochen Song, Qingwang Wang, Pengcheng Jin, Tao Shen

“…However, RS semantic segmentation is hindered by issues like class imbalance, occlusion, blurring, and small target sizes. …”
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Joined Spatial and Spectral Segmentation of Hyperspectral Datasets on Historical Art Objects by Lingxi Liu, Aurore Malmert, Emeline Pouyet, Silvia Mirri, Giovanni Delnevo

“…In the context of clustering and classification, the choice between spatial and spectral features hinges on data characteristics and analytical goals. …”
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The Diagnostic Classification of the Pathological Image Using Computer Vision by Yasunari Matsuzaka, Ryu Yashiro

“…Computer vision and artificial intelligence have revolutionized the field of pathological image analysis, enabling faster and more accurate diagnostic classification. Deep learning architectures like convolutional neural networks (CNNs), have shown superior performance in tasks such as image classification, segmentation, and object detection in pathology. …”
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Feature Extraction and Segmentation Methods in Plant Disease Detection: A Multimodal Approach by Thomas Kinyanjui Njoroge, Kevin Mugoye Sindu, Kibuku Rachael

“…However, while efficient, traditional machine learning methods often face challenges with generalization when trained on small datasets using basic features like shape, color, and texture. A promising approach to overcome this is the combination of deep feature extraction with machine learning classification, enabling more accurate disease detection. …”
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Review of iris segmentation and recognition using deep learning to improve biometric application by Rasheed Hind Hameed, Shamini Sara Swathy, Mahmoud Moamin A., Alomari Mohammad Ahmed

“…It was reviewed with iris image analysis, edge detection, and classification literature. DL improves iris segmentation and identification in biometric authentication, especially when combined with additional biometric modalities like fingerprint fusion. …”
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LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds by Veljka Kocic, Niko Lukac, Dzemail Rozajac, Stefan Schweng, Christoph Gollob, Arne Nothdurft, Karl Stampfer, Javier del Ser, Andreas Holzinger

“…Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. …”
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Hierarchical Transfer Learning with Transformers to Improve Semantic Segmentation in Remote Sensing Land Use by Miaomiao Chen, Lianfa Li

“…Land use classification remains a significant challenge in remote sensing semantic segmentation. …”
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Improving Cell Nuclei Segmentation in Pathological Tissues Using Self-Supervised Regression Method by Hesham Ali, Mostafa Hammouda, Mustafa Elattar, Sahar Selim

“…It incorporates various self-supervised strategies like image-scale regression and classification, denoising autoencoder, and relative positioning, with a focus on a scale-regression-based approach that significantly outperforms others. …”
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