arXiv Machine Learning

Automated binary classification of hazelnut X-ray images: A deep-learning benchmark for quality assessment

arXiv:2608. 11759v1 Announce Type: cross Abstract: Non-destructive X-ray imaging can reveal internal hazelnut defects that are difficult to detect by external inspection alone; however, automated interpretation remains challenging because of subtle radiographic differences among classes, marked class imbalance, and limited annotated data.

arXiv Machine Learning

Jun 18

Investigation of Neural Network Methods for Reconstruction and Classification of Texture Images Under Conditions of Incomplete Information

arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.

By Galymzhan Abdimanap, Kairat Bostanbekov, Abdelrahman Abdallah, Anel Alimova, Darkhan Kurmangaliyev, Daniyar Nurseitov, Tatyana Dedova, Larissa Balakay, Serik Nurakynov

llms agents diffusion computer-vision benchmarks safety

arXiv AI

Jun 2

Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift

arXiv:2606. 02045v1 Announce Type: cross Abstract: Artificial intelligence provides a practical framework for crop damage assessment from imagery data, supporting early decision-making in agricultural management.

By Adri\'an C\'anovas-Rodriguez, Miguel A. Gonz\'alez-Ill\'an, Maria Fernanda Garc\'ia-Cruz, Pedro Nortes Tortosa, Jos\'e Salvador Rubio-Asensio, Miguel A. Zamora Izquierdo, Juan Antonio Mart\'inez Navarro, Antonio F. Skarmeta

fine-tuning benchmarks

arXiv Machine Learning

Jul 9

Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops

arXiv:2607. 06585v1 Announce Type: cross Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in economic damages exceeding USD 220 billion.

By Raunak Kumar, Soumyashree Kar

computer-vision

arXiv AI

Jul 7

PulmoSight-XAI: An Explainable Multi-View Attention Ensemble with Gradient Boosting Meta-Learning for Multi-Label Chest X-Ray Classification

arXiv:2607. 04478v1 Announce Type: cross Abstract: Automated chest X-ray classification remains challenging due to severe class imbalance, co-occurring pathologies, and the loss of localized features in conventional architectures.

By Moshiur Rahman, Shafqat Alam, Tasnia Binte Mamun

benchmarks

arXiv Machine Learning

Jul 30

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance

arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.

By Panagiotis Fytas, Ian Selby, Clemens Karner, Judith Babar, Simon Baker, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, John Li Chen, Alex Sawer, Michael Roberts, Jonathan Weir-McCall, J. H. F. Rudd, Carola-Bibiane Sch\"onlieb, Anna Korhonen, Anna Breger

llms fine-tuning multimodal safety

arXiv Machine Learning

Jun 24

Machine Learning Modeling for Real-Time Melt Pool Monitoring in Laser Powder Bed Fusion Additive Manufacturing: A Hybrid Approach

arXiv:2606. 23851v1 Announce Type: new Abstract: This work investigates the implementation of artificial intelligence and machine learning (AI/ML) for real-time monitoring in laser powder bed fusion (LPBF) additive manufacturing.

By Inioluwa Emmanuel, Zhuo Yang, Ho Yeung, Xinyao Zhang

rag computer-vision fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 30

Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models

arXiv:2607. 26580v1 Announce Type: cross Abstract: With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately.

By Nand Lal Yadav, Rajesh Kumar, Satyendra Singh, Sudhakar Singh

arXiv Machine Learning

6d ago

Gaussian Meta-Space Augmentation for Stacking Ensembles in Multimodal IPMN Risk Stratification

arXiv:2608. 11472v1 Announce Type: cross Abstract: Pancreatic cancer is among the most lethal malignancies; risk stratification of intraductal papillary mucinous neoplasms (IPMNs) offers a crucial opportunity for early intervention but typically requires invasive tissue biopsy.

By Max A. Nelson, Eminenur Sen Tasci, Zhixiang Wang, Zongwei Zhou, Halil Ertugrul Aktas, Andrea M. Bejar, Elif Keles, Ziliang Hong, S{\i}tk{\i} Safa Taflan, Muhammed Enes Tasci, Frank H. Miller, Michael B. Wallace, Rajesh N. Keswani, Gorkem Durak, Ulas Bagci

multimodal

arXiv AI

Jun 15

CottonLeafVision: An Explainable and Robust Deep Learning Framework for Cotton Leaf Disease Classification

arXiv:2606. 14686v1 Announce Type: cross Abstract: Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it.

By Rafi Ahamed, Md. Abir Rahman, Tasnia Tarannum Roza, Munaia Jannat Easha, Md. Asif Khan, Sudeepta Mandal

safety

arXiv AI

Jun 16

An Ensemble Deep Learning Approach for Reliable and Scalable Lemon Leaf Disease Classification

arXiv:2606. 14871v1 Announce Type: cross Abstract: Early detection of plant diseases is crucial to plants and for the farmers.

By Shayan Abrar, Sudeepta Mandal, Abdul Awal Yasir, Sonjoy Bhattacharjee, Sadman Haque Bhuiyan, Samanta Ghosh, Rafi Ahamed

safety

arXiv AI

Jul 7

PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification

arXiv:2405. 07332v2 Announce Type: cross Abstract: Numerous applications have resulted from the automation of agricultural disease segmentation using deep learning techniques.

By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Mohammad Shafiul Alam, Ahmed Al Wase, Md. Rabius Sani, Khan Md Hasib

diffusion computer-vision safety

Hugging Face Trending Papers

Jul 6

ICME 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing

This paper presents the IEEE International Conference on Multimedia and Expo (ICME) 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing. The challenge is motivated by two key limitations of existing industrial defect inspection systems: (1) current deep learning-based methods often suffer significant performance degradation when deployed in unseen production scenarios, and (2) most benchmarks neglect severity-aware assessment, which is critical for risk control and yield optimization.

benchmarks

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