Machine learning applications in detection and diagnosis of urology cancers: a systematic literature review

A systematic review of AI as a digital twin for prostate cancer care

Artificial Intelligence (AI) and Digital Twin (DT) technologies are rapidly transforming healthcare, offering the potential for personalized, accurate, and efficient medical care. This systematic review focuses on the intersection of AI-based digital twins and their applications in prostate cancer pathology. A digital twin, when applied to healthcare, creates a dynamic, data-driven virtual model that simulates a patient's biological systems in real-time. By incorporating AI techniques such as Machine Learning (ML) and Deep Learning (DL), these systems enhance predictive accuracy, enable early diagnosis, and facilitate individualized treatment strategies for prostate cancer. This review systematically examines recent advances (2020-2025) in AI-driven digital twins for prostate cancer, highlighting key methodologies, algorithms, and data integration strategies. The literature analysis also reveals substantial progress in image processing, predictive modeling, and clinical decision support systems, which are the basic tools used when implementing digital twins for prostate cancer care. Our survey also critically evaluates the strengths and limitations of current approaches, identifying gaps such as the need for real-time data integration, improved explainability in AI models, and more robust clinical validation. It concludes with a discussion of future research directions, emphasizing the importance of integrating multi-modal data with Large Language Models (LLMs) and Vision-Language Models (VLMs), scalability, and ethical considerations in advancing AI-driven digital twins for prostate cancer diagnosis and treatment. This paper provides a comprehensive resource for researchers and clinicians, offering insights into how AI-based digital twins can enhance precision medicine and improve patient outcomes in prostate cancer care.

An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images

The importance of gastric cancer (GC) and the role of deep learning techniques in categorizing GC histopathology images have recently increased. Identifying the drawbacks of traditional deep learning models, including lack of interpretability, inability to capture complex patterns, lack of adaptability, and sensitivity to noise. A multi-channel attention mechanism-based framework is proposed that can overcome the limitations of conventional deep learning models by dynamically focusing on relevant features, enhancing extraction, and capturing complex relationships in medical data. The proposed framework uses three different attention mechanism channels and convolutional neural networks to extract multichannel features during the classification process. The proposed framework's strong performance is confirmed by competitive experiments conducted on a publicly available Gastric Histopathology Sub-size Image Database, which yielded remarkable classification accuracies of 99.07% and 98.48% on the validation and testing sets, respectively. Additionally, on the HCRF dataset, the framework achieved high classification accuracy of 99.84% and 99.65% on the validation and testing sets, respectively. The effectiveness and interchangeability of the three channels are further confirmed by ablation and interchangeability experiments, highlighting the remarkable performance of the framework in GC histopathological image classification tasks. This offers an advanced and pragmatic artificial intelligence solution that addresses challenges posed by unique medical image characteristics for intricate image analysis. The proposed approach in artificial intelligence medical engineering demonstrates significant potential for enhancing diagnostic precision by achieving high classification accuracy and treatment outcomes.

A quantum inspired machine learning approach for multimodal Parkinson's disease screening

Parkinson's disease, currently the fastest-growing neurodegenerative disorder globally, has seen a 50% increase in cases within just two years. As disease progression impairs speech, memory, and motor functions over time, early diagnosis is crucial for preserving patients' quality of life. Although machine-learning-based detection has shown promise for detecting Parkinson's disease, most studies rely on a single feature for classification and can be error-prone due to the variability of symptoms between patients. To address this limitation we utilized the mPower dataset, which includes 150,000 samples across four key biomarkers: voice, gait, tapping, and demographic data. From these measurements, we extracted 64 features and trained a baseline Random Forest model to select the features above the 80th percentile. For classification, we designed a simulatable quantum support vector machine (qSVM) that detects high-dimensional patterns, leveraging recent advancements in quantum machine learning. With this novel and simulatable architecture that can be run on standard hardware rather than resource-intensive quantum computers, our model achieves an accuracy of 90%, F-1 score of 0.90, and an AUC of 0.98-surpassing benchmark models. Utilizing an innovative classification framework built on a diverse set of features, our model offers a pathway for accessible global Parkinson's screening.

A BiLSTM model enhanced with multi-objective arithmetic optimization for COVID-19 diagnosis from CT images

In response to the relentless mutation of the coronavirus disease, current artificial intelligence algorithms for the automated diagnosis of COVID-19 via CT imaging exhibit suboptimal accuracy and efficiency. This manuscript proposes a multi-objective optimization algorithm (MOAOA) to enhance the BiLSTM model for COVID-19 automated diagnosis. The proposed approach involves configuring several hyperparameters for the bidirectional long short-term memory (BiLSTM), optimized using the MOAOA intelligent optimization algorithm, and subsequently validated on publicly accessible medical datasets. Remarkably, our model achieves an impressive 95.32% accuracy and 95.09% specificity. Comparative analysis with state-of-the-art techniques demonstrates that the proposed model significantly enhances accuracy, efficiency, and other performance metrics, yielding superior results.

Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks

Parkinson's disease is a neurodegenerative disorder that is associated with aging, leading to the progressive deterioration of certain regions of the brain. Accurate and timely diagnosis plays a crucial role in facilitating optimal therapy and improving patient outcomes. This study presents an innovative approach to identify Parkinson's disease (PD) through the examination of audio waves using Feature Based - Deep Neural Network (FB-DNN) techniques. Autoencoder, a specific form of Artificial Neural Network (ANN) that is designed to excel in the task of feature extraction, is utilized in our study to effectively capture complex patterns present in audio data. Deep Neural Networks (DNNs) are utilized in the task of classification, using the capabilities of deep learning (DL) to differentiate between audio samples that exhibit Parkinson's disease (PD) and those that do not. The deep neural network (DNN) model is trained using the retrieved data, allowing it to effectively distinguish minor variations in voice characteristics that are linked to Parkinson's disease. The suggested methodology not only enhances the precision of diagnosis but also enables prompt identification, perhaps resulting in more efficacious treatment methodologies. The present study introduces a potentially effective approach for the automated and non-intrusive identification of Parkinson's disease through the analysis of audio data. The integration of Autoencoder-based feature extraction with Deep Neural Networks (DNN) presents a dependable and easily accessible solution for the early detection and continuous monitoring of Parkinson's disease. This approach has promise for significantly improving the quality of life for persons affected by this condition. The implementation in Python was conducted as part of our experimentation. Upon analyzing the accuracy, it became apparent that the Feature-Based Deep Neural Network (FB-DNN) exhibited superior performance compared to the other models. Notably, the FB-DNN achieved the highest accuracy score of 96.15%.

A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases

Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates and long-term disabilities. Early diagnosis, particularly within the first hours, is crucial for preventing irreversible damage and improving patient outcomes. Although neuroimaging techniques like magnetic resonance imaging (MRI) have advanced significantly, traditional methods often fail to fully capture the complexity of brain lesions. Deep learning has recently emerged as a powerful tool in medical imaging, offering high accuracy in detecting and segmenting brain anomalies. This review examines 61 MRI-based studies published between 2020 and 2024, focusing on the role of deep learning in diagnosing cerebral vascular occlusion-related conditions. It evaluates the successes and limitations of these studies, including the adequacy and diversity of datasets, and addresses challenges such as data privacy and algorithm explainability. Comparisons between convolutional neural network (CNN)-based and Vision Transformer (ViT)-based approaches reveal distinct advantages and limitations. The findings emphasize the importance of ethically secure frameworks, the inclusion of diverse datasets, and improved model interpretability. Advanced architectures like U-Net variants and transformer-based models are highlighted as promising tools to enhance reliability in clinical applications. By automating complex neuroimaging tasks and improving diagnostic accuracy, deep learning facilitates personalized treatment strategies. This review provides a roadmap for integrating technical advancements into clinical practice, underscoring the transformative potential of deep learning in managing neurological disorders and improving healthcare outcomes globally.

A robust deep learning framework for multiclass skin cancer classification

Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and patient survival rates. Nonetheless, the inherent visual similarities between benign and malignant lesions pose substantial challenges to accurate classification. To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks and separable self-attention mechanisms, tailored to enhance feature extraction and optimize classification performance. The inclusion of ConvNeXtV2 blocks in the initial two stages is driven by their ability to effectively capture fine-grained local features and subtle patterns, which are critical for distinguishing between visually similar lesion types. Meanwhile, the adoption of separable self-attention in the later stages allows the model to selectively prioritize diagnostically relevant regions while minimizing computational complexity, addressing the inefficiencies often associated with traditional self-attention mechanisms. The model was comprehensively trained and validated on the ISIC 2019 dataset, which includes eight distinct skin lesion categories. Advanced methodologies such as data augmentation and transfer learning were employed to further enhance model robustness and reliability. The proposed architecture achieved exceptional performance metrics, with 93.48% accuracy, 93.24% precision, 90.70% recall, and a 91.82% F1-score, outperforming over ten Convolutional Neural Network (CNN) based and over ten Vision Transformer (ViT) based models tested under comparable conditions. Despite its robust performance, the model maintains a compact design with only 21.92 million parameters, making it highly efficient and suitable for model deployment. The Proposed Model demonstrates exceptional accuracy and generalizability across diverse skin lesion classes, establishing a reliable framework for early and accurate skin cancer diagnosis in clinical practice.

A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx

"PolynetDWTCADx" is a sophisticated hybrid model that was developed to identify and distinguish colorectal cancer. In this study, the CKHK-22 dataset, comprising 24 classes, served as the introduction. The proposed method, which combines CNNs, DWTs, and SVMs, enhances the accuracy of feature extraction and classification. The study employs DWT to optimize and enhance two integrated CNN models before classifying them with SVM following a systematic procedure. PolynetDWTCADx was the most effective model that we evaluated. It was capable of attaining a moderate level of recall, as well as an area under the curve (AUC) and accuracy during testing. The testing accuracy was 92.3%, and the training accuracy was 95.0%. This demonstrates that the model is capable of distinguishing between noncancerous and cancerous lesions in the colon. We can also employ the semantic segmentation algorithms of the U-Net architecture to accurately identify and segment cancerous colorectal regions. We assessed the model's exceptional success in segmenting and providing precise delineation of malignant tissues using its maximal IoU value of 0.93, based on intersection over union (IoU) scores. When these techniques are added to PolynetDWTCADx, they give doctors detailed visual information that is needed for diagnosis and planning treatment. These techniques are also very good at finding and separating colorectal cancer. PolynetDWTCADx has the potential to enhance the recognition and management of colorectal cancer, as this study underscores.

Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP

Skin cancer is one of the most frequently occurring cancers worldwide, and early detection is crucial for effective treatment. Dermatologists often face challenges such as heavy data demands, potential human errors, and strict time limits, which can negatively affect diagnostic outcomes. Deep learning-based diagnostic systems offer quick, accurate testing and enhanced research capabilities, providing significant support to dermatologists. In this study, we enhanced the Swin Transformer architecture by implementing the hybrid shifted window-based multi-head self-attention (HSW-MSA) in place of the conventional shifted window-based multi-head self-attention (SW-MSA). This adjustment enables the model to more efficiently process areas of skin cancer overlap, capture finer details, and manage long-range dependencies, while maintaining memory usage and computational efficiency during training. Additionally, the study replaces the standard multi-layer perceptron (MLP) in the Swin Transformer with a SwiGLU-based MLP, an upgraded version of the gated linear unit (GLU) module, to achieve higher accuracy, faster training speeds, and better parameter efficiency. The modified Swin model-base was evaluated using the publicly accessible ISIC 2019 skin dataset with eight classes and was compared against popular convolutional neural networks (CNNs) and cutting-edge vision transformer (ViT) models. In an exhaustive assessment on the unseen test dataset, the proposed Swin-Base model demonstrated exceptional performance, achieving an accuracy of 89.36%, a recall of 85.13%, a precision of 88.22%, and an F1-score of 86.65%, surpassing all previously reported research and deep learning models documented in the literature.

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model’s ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model’s decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.