Monkeypox was declared a public health emergency of international concern by WHO in July 2022 due to the unprecedented global spread of the disease outside of previously endemic countries in Africa. Previously proposed classification models based mainly on pre-trained CNNs are ineffective in detecting Monkeypox. In this paper, a novel model is presented for the Monkeypox virus detection hybrid window attention and depthwise asymmetric dilation convolution. This study's novelty lies in the proposed lightweight and robust multi-stage model, utilizing depthwise dilation convolution and pointwise convolution in the two first stages with a large spatial dimension. Window attention combination with depthwise asymmetric dilation convolution is applied at the two last stages to reduce model complexity. We have evaluated the proposed approach with ViT, Swin Transformer, and MaxViT, DenseNet201, and ResNet50 on the public dataset of “Monkeypox Skin Images Dataset” (MSID) on Mendeley. The model performance was evaluated using metrics such as accuracy, recall, precision, specificity, and F1-score. The proposed method achieves the best results with an average of 99.30%, 98.65%, 98.60%, 99.56%, and 98.60% in accuracy, precision, recall, and specificity, F1-score., respectively. Additionally, the proposed model has only 19M parameters, which is smaller than MaxViT and Swin Transformer.

Vehicle damage detection plays a vital role in assessing damage severity and estimating repair costs, which are essential for efficient insurance claim processing. However, this task is still predominantly manual, making it time-consuming and prone to errors or fraud due to human involvement. This paper presents an experimental comparative analysis of state-of-the-art object detection models for identifying and classifying vehicle damage. The evaluated models include RT-DETR, YOLOv12n, YOLOv11n, and YOLOv8n. Experimental results indicate that the models perform differently in detecting small and complex damage areas and in maintaining stable performance under varying lighting and viewing conditions. RT-DETR achieves the highest mean Average Precision (mAP) of 54.1%, outperforming YOLOv11n (47.0%), YOLOv8n (46.2%), and YOLOv12n (48.1%). This analysis highlights the potential of object detection models in the domain of vehicle damage assessment, contributing to cost reduction, improved transparency, and more efficient insurance claim workflows.

Breast cancer is a dangerous disease, contributing to a high mortality rate in women. Early detection plays a pivotal role in enhancing survival rates. Breast ultrasound is considered an effective method to help diagnose breast diseases early. Breast ultrasound is inexpensive, easy to perform, non-invasive and painless, so it is often prescribed by doctors in cases where it is necessary to examine the nature of clinically palpable lesions or related symptoms in the breast. In this paper, we introduce a method based on transfer learning and deep feature fusion to classify breast cancer using ultrasound images. The results from our experiments involving 780 breast ultrasound images across three categories (benign, malignant, and normal) indicated that the model using max fusion of deep features outperformed an original CNN in terms of performance, the combination of the maximum value between deep features has a higher performance level with an accuracy of about 1% to 4% compared to the original model. The concatenation fusion of VGG19 and ViT features delivers 1% - 4% times more accuracy than the original model alone

Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the computational process that recovers a more informative image. It is particularly challenging with thick and complex samples such as organoids. Recent works that rely on supervised training show that deep learning is a powerful method to unwrap the phase; however, supervised approaches require large and representative datasets which are difficult to obtain for complex biological samples. Inspired by the concept of deep image priors, we propose a deep-learning-based method that does not need any training set. Our framework relies on an untrained convolutional neural network to accurately unwrap the phase while ensuring the consistency of the measurements. We experimentally demonstrate that the proposed method faithfully recovers the phase of complex samples on both real and simulated data. Our work paves the way to reliable phase imaging of thick and complex samples with QPI.