Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation, however, is labor-intensive, operator-dependent, and limited by inter- and intra-observer variability, motivating the development of objective and reproducible computational approaches.
arXiv:2606. 25463v1 Announce Type: cross Abstract: This study introduces Blasto-Net, a multi-task deep learning model for comprehensive blastocyst analysis.
By Zahra Asghari Varzaneh, Reza Khoshkangini, Magnus Johnsson, Thomas Ebner, Lars Johansson
STA‑Net is a lightweight neural network designed for plant disease classification on edge devices. It combines a training‑free neural architecture search (DeepMAD) to build an efficient backbone with a novel Shape‑Texture Attention Module (STAM) that separates shape and texture processing using deformable convolutions and a Gabor filter bank. On the CCMT plant disease dataset, STA‑Net achieved 89.00% accuracy and 88.96% F1 score with only 401K parameters and 51.1M FLOPs.
By Zongsen Qiu, Jianjun Wang, Yue Zhou, Zibo Zhou, Rui Chen
arXiv:2511. 18454v3 Announce Type: replace-cross Abstract: Embryo fragmentation is a morphological indicator critical for evaluating developmental potential in In Vitro Fertilization (IVF).
By Ming-Jhe Lee, Chang-Hong Wu, Jung-Hua Wang, Ming-Jer Chen, Yu-Chiao Yi, Tsung-Hsien Lee
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
The paper introduces an attention‑guided fusion framework that combines global and lesion‑focused local information for image classification. Using a three‑branch architecture built on DenseNet‑121, the model generates attention maps with Grad‑CAM, refines local features with CBAM, and adaptively fuses the two representations. Experiments on synthetic and real datasets, including skin, guava leaf, and grape leaf images, show that the fusion branch outperforms individual branches, achieving up to 97.75% accuracy on skin lesions and 99.64% on guava leaves.
By Mst Shafia Tasnima, Md Samaun Elaheea, Tanjim Taharat Aurpab, Md Musfique Anwar
arXiv:2511. 01143v2 Announce Type: replace-cross Abstract: Early and accurate segmentation of colorectal polyps is critical for reducing colorectal cancer mortality, which has been extensively explored by academia and industry.
By Ziyi Wang, Yuanmei Zhang, Baoying Ye, Yimei Jiang, Leilei Gu, Suncheng Xiang
arXiv:2607. 16936v1 Announce Type: cross Abstract: Pediatric bone age prediction is a crucial task in clinical practice that can help diagnose endocrine disorders and provide insight into a child's growth and development.
By Al Zadid Sultan Bin Habib, Md. Ekramul Islam, Md Asif Bin Syed, Md Younus Ahamed, Tanpia Tasnim
arXiv:2609.14278v1 Announce Type: cross
Abstract: Accurate sperm detection is essential for computer-assisted semen analysis, yet it remains challenging in microscopic images due to dense distributio...
By Shengqi Chen, Zilin Wang, Xingyu Pan, Wenting Yu, Pengchao Deng, Guohua Wu
arXiv:2508. 17728v2 Announce Type: replace-cross Abstract: Cervical cancer remains a significant global health concern and a leading cause of cancer-related deaths among women.
By Nisreen Albzour, Sarah S. Lam
arXiv:2507. 05077v5 Announce Type: replace-cross Abstract: Deep neural networks are increasingly applied in automated histopathology.
By Tarun Gogisetty, Naman Malpani, Gugan Thoppe, Sridharan Devarajan
arXiv:2607.10851v2 Announce Type: replace
Abstract: Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classif...
By Tonmoy Hossain, Atiqur Rahman, Farhana Hossain Swarnali, Miaomiao Zhang