Quantum-infused deep learning for dual-task thyroid ultrasound diagnostics

RLU-Net-GD: a deep supervised residual U-Net for enhanced brain tumor segmentation

Attention-Guided Ensemble Deep Learning Framework for Automated Skin Cancer Classification

Skin cancer is one of the most common malignancies in the world and early and accurate dermoscopic diagnosis is crucial for better survival outcomes of patients. There are various limitations in current single model convolutional and transformer models, such as limited ability to capture local texture, multi-scale morphology, and global contextual information, as well as high intra-class visual similarity and extreme class imbalance. To overcome these problems, this paper proposes an Attention-Guided Ensemble Deep Learning (AGEDL) framework which integrates the EfficientNet-B3, InceptionV3 and Swin Transformer to simultaneously learn complementary dermoscopic representations from the seven-class ISIC 2018 dataset. The Squeeze-and-Excitation (SE) attention blocks dynamically modulate feature responses in each channel, which reduces background noise and enhances discriminative features of lesions. The Lion optimizer offers stable and efficient training in both base training and fine-tuning stages. An XGBoost stacking metalearner is used to combine all backbone networks, and hyperparameters of the XGBoost are optimized by the Dhole Optimizer. The proposed AGEDL achieved 98.02% accuracy, 97.09% precision, 97.05% recall and 94.07% F1 score with 95% confidence interval of 97.41%–98.59%, surpassing the state-of-the-art CNN, transformer-based and hybrid ensemble baselines.

EFFICIENTNET-BASED MULTI-CLASS BRAIN TUMOR CLASSIFICATION FROM MRI IMAGES: A ROBUST DEEP LEARNING FRAMEWORK

The study concludes that model efficiency alone is insufficient; dependable brain tumor classification requires disciplined data handling, transparent reporting, and external validation, and external validation.

A Novel Explainable Deep Learning Model for Early-Stage Brain Tumor Classification: Multi-Level Feature Fusion from Merged MRI Datasets

A unique explainable deep learning model that integrates Tiny-ConvNeXt and DenseNet169 using multi-stage brain tumor classification is proposed, indicating that the suggested model provides a transparent and reliable framework for brain tumor detection, with potential applications in practical clinical decision support systems.

A hybrid hierarchical multimodal learning with autoencoders, vision transformers and deep belief networks for liver cancer detection

A clinical problem of early liver cancer is one of the most urgent ones due to the insidious nature of tumors, the lack of multimodal data consistency and large inter-subject variation. Conventional systems of diagnosing rely highly on manual interpretation and monomodality images that typically result in delayed diagnosis and reduced effectiveness in the treatment. The researcher in this study presents another model of multimodal deep intelligence to detect and categorize early liver cancer through the combination of magnetic resonance imaging (MRI) and computed tomography (CT) data. The provided solution is an integrated self-regularized autoencoder (AE) to learn the multimodal representation and allows noise suppression and compression of latent features. Vision Transformer (ViT) involves extracting long-range spatial dependencies and context in fused representations. Dynamic maximization of diagnostic decision policy and classification confidence under different clinical settings ML networks are A Deep Belief Network (DBN) that has hierarchical tissue abstraction and probabilistic pattern modelling and Deep Q-Network (DQN) that optimizes diagnostic decision policy and classification confidence. A standard liver imaging dataset (612 institutional patients to be assessed in the first instance and 512 public patients to be assessed in the second instance) yields high diagnostic quality, potential for generalization, and classification accuracy with positive results of 98.76% (95% CI 98.12–99.34) and an AUC of 0.992 (95% The findings confirm that there was successful multimodal fusion with heterogeneous deep learning paradigms in the detection of early liver cancer. This research will add the presence of scalable, intelligent, and clinically relevant diagnostic architecture to facilitate timely responses and decision support systems to manage liver cancer.

Quantum‐Guided Textural–Spatial Fusion Network for Thyroid Nodule Classification in Ultrasound Imaging

Thyroid cancer is among the most common malignancies, and ultrasound is the primary imaging modality due to its safety and wide availability. Accurate diagnosis remains difficult because of low contrast, acoustic shadowing, and subtle visual differences between benign and malignant nodules. These factors increase observer variability and lead to missed or incorrect diagnoses. Prior deep learning methods address this problem only partially by selectively focusing on spatial information and failing to capture long‐range dependencies. Other approaches rely on handcrafted texture descriptors and overlook critical grayscale patterns in ultrasound. This paper presents a Q uantum‐guided Tex tural– S patial Fusion Net work (QTexS‐Net) for thyroid nodule classification in ultrasound imaging. The proposed framework jointly learns complementary spatial and textural representations. A spatial‐aware autoencoder captures local structures and global contextual dependencies, while a texture‐aware branch preserves fine‐grained grayscale characteristics relevant to malignancy. A quantum‐inspired fusion mechanism models interactions between spatial and textural features, leading to more discriminative representations and improved training efficiency. Experiments on three public benchmark datasets show that QTexS‐Net consistently outperforms existing methods. Ablation studies and cross‐dataset evaluations confirm the contribution of each component. Explainability analyses further highlight clinically meaningful regions, supporting reliable, interpretable decision‐making in thyroid nodule assessment and providing transparent decision support for clinicians.