Radiology experts often face difficulties in mammography mass lesion labeling, which may lead to conclusive yet unnecessary and expensive breast biopsies. This paper focuses on building an automated diagnosis tool that supports radiologists in identifying and classifying mammography mass lesions. The paper's main contribution is to design a hybrid model based on Pulse-Coupled Neural Networks (PCNN) and Deep Convolutional Neural Networks (CNN). Due to the need for large datasets to train and tune CNNs, which are not available for medical images, Transfer Learning (TL) was exploited in this research. TL can be an effective approach when working with small-sized datasets. The paper's implementation was tested on three public benchmark datasets: DDMS, INbreast, and BCDR datasets for training and testing and MIAS for testing only. The results indicated the enhancement that PCNN provides when combined with CNN compared to other methods for the same public datasets. The hybrid model achieved 98.72% accuracy for DDMS, 97.5% for INbreast, and 96.94% for BCDR. To avoid overfitting, the proposed hybrid model was tested on an unseen MIAS dataset, achieving 98.77% accuracy. Other evaluation metrics are reported in the results section.

The overall healthcare system has been prioritized within development top lists worldwide. Since many national populations are aging, combined with the availability of sophisticated medical treatments, healthcare expenditures are rapidly growing. Blood banks are a major component of any healthcare system, which store and provide the blood products needed for organ transplants, emergency medical treatments, and routine surgeries. Timely delivery of blood products is vital, especially in emergency settings. Hence, blood delivery process parameters such as safety and speed have received attention in the literature, as well as other parameters such as delivery cost. In this paper, delivery time and cost are modeled mathematically and marked as objective functions requiring simultaneous optimization. A solution is proposed based on Deep Reinforcement Learning (DRL) to address the formulated delivery functions as Multi-objective Optimization Problems (MOPs). The basic concept of the solution is to decompose the MOP into a scalar optimization sub-problems set, where each one of these sub-problems is modeled as a separate Neural Network (NN). The overall model parameters for each sub-problem are optimized based on a neighborhood parameter transfer and DRL training algorithm. The optimization step for the sub-problems is undertaken collaboratively to optimize the overall model. Pareto-optimal solutions can be directly obtained using the trained NN. Specifically, the multi-objective blood bank delivery problem is addressed in this research. One major technical advantage of this approach is that once the trained model is available, it can be scaled without the need for model retraining. The scoring can be obtained directly using a straightforward computation of the NN layers in a limited time. The proposed technique provides a set of technical strength points such as the ability to generalize and solve rapidly compared to other multi-objective optimization methods. The model was trained and tested on 5 major hospitals in Saudi Arabia’s Riyadh region, and the simulation results indicated that time and cost decreased by 35% and 30%, respectively. In particular, the proposed model outperformed other state-of-the-art MOP solutions such as Genetic Algorithms and Simulated Annealing.

Background/Objectives: Melanoma is a life-threatening skin cancer characterized by aggressive progression and high metastatic potential, making early diagnosis essential for improving patient survival and treatment outcomes. However, accurate automated skin lesion classification remains challenging due to variations in lesion appearance, illumination, image quality, and the presence of artifacts. This study proposes a unified framework for robust multi-class skin cancer classification by integrating preprocessing, lesion segmentation, feature extraction, optimization, and classification within a single end-to-end architecture. Methods: The proposed framework employs an iterative hair artifact removal strategy based on the fusion of Frangi vesselness filtering, Gabor texture filtering, morphological refinement, and Telea inpainting to preserve lesion integrity. A novel dermoscopic lesion segmentation network (CutisNet) is introduced to accurately delineate lesion boundaries. Hybrid representation learning combines deep features extracted using MobileNetV2 with uniquely selected handcrafted descriptors to capture complementary texture, structural, and contextual information. Gray Wolf Optimization is utilized for feature fusion and refinement, while a hybrid GNN–CNN classifier performs robust multi-class skin lesion classification. Results: Extensive experiments conducted on multiple benchmark dermoscopic datasets demonstrate the effectiveness and generalization capability of the proposed framework. The proposed model consistently outperformed existing state-of-the-art methods, achieving a maximum classification accuracy of 96.7% on the PH² dataset while maintaining competitive performance across other benchmark datasets. Conclusions: The proposed framework effectively integrates novel preprocessing, segmentation, hybrid feature representation, feature optimization, and classification strategies to improve the robustness and accuracy of automated skin cancer classification. These results demonstrate its potential to support reliable computer-aided diagnosis and assist clinicians in the early detection of skin cancer.

Accurate forecasting of severe convective events is vital for meteorologists, as it directly supports their efforts to understand atmospheric risk patterns and enable effective early-warning systems. This paper presents an Integrating Heuristic Knowledge with Attention-based LSTM Networks for Thunderstorm Prediction (HoST) for probabilistic thunderstorm prediction. The proposed framework integrates attention-enhanced recurrent modelling with physically informed heuristic constraints, allowing the model to capture complex nonlinear atmospheric dynamics while maintaining meteorological consistency. The model is evaluated on a real-world observational dataset, where it demonstrates strong predictive capability in capturing spatiotemporal convective patterns. Furthermore, the framework is assessed in a quasi-operational forecasting setting, exhibiting low-latency inference, computational efficiency, and stable predictive performance across multiple forecast lead times (5-60 minutes). To further validate the robustness of the approach, controlled experiments are conducted using synthetically generated atmospheric scenarios that emulate key thermodynamic and kinematic relationships. Results show improved classification stability, enhanced probabilistic calibration, and superior performance compared to Random Forest, SVM, Improved Decision Support, Deep Neural Network, SALAMA, BLSTM-GRU, MetNet, FourCastNet, GraphCast, HRRR, and AROME models. Overall, the findings highlight the effectiveness of integrating heuristic knowledge with data-driven learning, demonstrating the potential of HoST as a physically consistent and operationally viable framework for short-term thunderstorm forecasting.