The firefly algorithm (FA) is a nature-inspired metaheuristic increasingly applied to medical image segmentation because of its flexible search mechanism and optimization capability. This paper systematically reviews FA-based approaches for medical image segmentation and related image-analysis tasks published between 2010 and 2026. A structured search of Scopus, PubMed, IEEE Xplore, Web of Science, and Google Scholar was conducted using predefined eligibility criteria. The included studies were categorized into multilevel thresholding, entropy-based segmentation, clustering-assisted methods, adaptive and chaotic FA variants, and hybrids with deep learning and other metaheuristics. Major appl... More >

Brain tumor segmentation plays an important role in clinical diagnosis, treatment planning, and neuro-oncology assessment using multimodal Magnetic Resonance Imaging (MRI) data. However, conventional centralized deep learning systems often face limitations associated with patient data privacy, secure inter-institutional collaboration, and limited model interpretability. This study presents a decentralized and privacy-preserving brain tumor segmentation framework that integrates Federated Learning (FL), a blockchain-inspired audit and coordination mechanism, and Explainable Artificial Intelligence (XAI) within a collaborative medical imaging environment. A 3D U-Net architecture was trained on... More >

Skin lesion classification is critical in dermatological diagnosis, where early and accurate identification of malignant lesions can significantly improve patient outcomes. Deep learning approaches, particularly transfer learning with pre-trained CNNs, have demonstrated remarkable performance in automated dermoscopic image analysis. However, the optimal configuration of transfer learning components---including backbone architecture, fine-tuning strategy, and data augmentation intensity---remains an open question. In this paper, we present a systematic comparative study on the HAM10000 dataset, evaluating three CNN architectures (ResNet50, DenseNet121, EfficientNet-B0), three fine-tuning stra... More >

Early recognition of maize leaf disorders and applying precautionary measures on time may help to increase the yield and quality. This study introduces an architecture for the recognition and categorization of maize leaf diseases based on the deep Inception-v3 and maximum value-based color features. The core steps of the designed framework include data acquisition, feature extraction, fusion, and classification. The maize leaf image dataset is utilized, which is publicly available on Kaggle, comprising four classes. The deep learning features are collected by applying the transfer learning approach to the pre-trained Inception-v3 model. In addition to the deep features, maximum value-based c... More >

Breast cancer is a leading cause of cancer-related mortality worldwide, making accurate histopathological subtype discrimination critical for timely clinical intervention. Existing deep learning approaches often evaluate limited settings (binary or multi-class, single magnification), restricting comparative utility and clinical interpretability. This study proposes a unified Cross Stage Partial Network (CSPNet)-based framework for comprehensive classification on the BreaKHis dataset. A CSPResNet50 backbone pre-trained on ImageNet was extended with a multi-scale Feature Pyramid-style aggregation head, Squeeze-and-Excitation channel attention, dual Global Average and Max Pooling per scale (153... More >