efficientnetb7-based-deep-learning-framework-for-enhanced

EfficientNetB7-Based Deep Learning Framework for Enhanced Classification of Lung and Colon Cancer Histopathological Images

Early diagnosis of lung cancer plays a pivotal role in ensuring improved treatment and survival of patients. This remains a major focus in clinical ...

an-explainable-ai-based-transfer-learning-method-for-breast-cancer

An Explainable AI-Based Transfer Learning Method for Breast Cancer Prediction

Breast cancer identification via ultrasound images requires high accuracy and transparency to assist clinicians in taking appropriate decisions. This work ...

: Liver cancer ranks among the leading causes of cancer-related mortality, necessitating the development of novel diagnostic methods. Deregulated lipid metabolism, a hallmark of hepatocarcinogenesis, offers compelling prospects for biomarker identification. This study aims to employ explainable artificial intelligence (XAI) to identify lipidomic biomarkers for liver cancer and to develop a robust predictive model for early diagnosis. : This study included 219 patients diagnosed with liver cancer and 219 healthy controls. Serum samples underwent untargeted lipidomic analysis with LC-QTOF-MS. Lipidomic data underwent univariate and multivariate analyses, including fold change (FC), -tests, PLS-DA, and Elastic Network feature selection, to identify significant biomarker candidate lipids. Machine learning models (AdaBoost, Random Forest, Gradient Boosting) were developed and evaluated utilizing these biomarkers to differentiate liver cancer. The AUC metric was employed to identify the optimal predictive model, whereas SHAP was utilized to achieve interpretability of the model's predictive decisions. : Notable alterations in lipid profiles were observed: decreased sphingomyelins (SM d39:2, SM d41:2) and increased fatty acids (FA 14:1, FA 22:2) and phosphatidylcholines (PC 34:1, PC 32:1). AdaBoost exhibited a superior classification performance, achieving an AUC of 0.875. SHAP identified PC 40:4 as the most efficacious lipid for model predictions. The SM d41:2 and SM d36:3 lipids were specifically associated with an increased risk of low-onset cancer and elevated levels of the PC 40:4 lipid. : This study demonstrates that untargeted lipidomics, in conjunction with explainable artificial intelligence (XAI) and machine learning, may effectively identify biomarkers for the early detection of liver cancer. The results suggest that alterations in lipid metabolism are crucial to the progression of liver cancer and provide valuable insights for incorporating lipidomics into precision oncology.

Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence.

Cerebral vascular occlusion is a serious condition that can lead to stroke and permanent neurological damage due to insufficient oxygen and nutrients reaching brain tissue. Early diagnosis and accurate segmentation are critical for effective treatment planning. Due to its high soft tissue contrast, Magnetic Resonance Imaging (MRI) is commonly used for detecting these occlusions such as ischemic stroke. However, challenges such as low contrast, noise, and heterogeneous lesion structures in MRI images complicate manual segmentation and often lead to misinterpretations. As a result, deep learning-based Computer-Aided Diagnosis (CAD) systems are essential for faster and more accurate diagnosis and treatment methods, although they can sometimes face challenges such as high computational costs and difficulties in segmenting small or irregular lesions. This study proposes a novel U-Net architecture enhanced with ConvNeXtV2 blocks and GRN-based Multi-Layer Perceptrons (MLP) to address these challenges in cerebral vascular occlusion segmentation. This is the first application of ConvNeXtV2 in this domain. The proposed model significantly improves segmentation accuracy, even in low-contrast regions, while maintaining high computational efficiency, which is crucial for real-world clinical applications. To reduce false positives and improve overall accuracy, small lesions (≤5 pixels) were removed in the preprocessing step with the support of expert clinicians. Experimental results on the ISLES 2022 dataset showed superior performance with an Intersection over Union (IoU) of 0.8015 and a Dice coefficient of 0.8894. Comparative analyses indicate that the proposed model achieves higher segmentation accuracy than existing U-Net variants and other methods, offering a promising solution for clinical use.

Deep learning for cerebral vascular occlusion segmentation: A novel ConvNeXtV2 and GRN-integrated U-Net framework for diffusion-weighted imaging.

: Breast cancer (BC) is the most common type of cancer in women, accounting for more than 30% of new female cancers each year. Although various treatments are available for BC, most cancer-related deaths are due to incurable metastases. Therefore, the early diagnosis and treatment of BC are crucial before metastasis. Mammography and ultrasonography are primarily used in the clinic for the initial identification and staging of BC; these methods are useful for general screening but have limitations in terms of sensitivity and specificity. Omics-based biomarkers, like metabolomics, can make early diagnosis much more accurate, make tracking the disease's progression more accurate, and help make personalized treatment plans that are tailored to each tumor's specific molecular profile. Metabolomics technology is a feasible and comprehensive method for early disease detection and biomarker identification at the molecular level. This research aimed to establish an interpretable predictive artificial intelligence (AI) model using plasma-based metabolomics panel data to identify potential biomarkers that distinguish BC individuals from healthy controls. : A cohort of 138 BC patients and 76 healthy controls were studied. Plasma metabolites were examined using LC-TOFMS and GC-TOFMS techniques. Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Random Forest (RF) were evaluated using performance metrics such as Receiver Operating Characteristic-Area Under the Curve (ROC AUC), accuracy, sensitivity, specificity, and F1 score. ROC and Precision-Recall (PR) curves were generated for comparative analysis. The SHapley Additive Descriptions (SHAP) analysis evaluated the optimal prediction model for interpretability. : The RF algorithm showed improved accuracy (0.963 ± 0.043) and sensitivity (0.977 ± 0.051); however, LightGBM achieved the highest ROC AUC (0.983 ± 0.028). RF also achieved the best Precision-Recall Area under the Curve (PR AUC) at 0.989. SHAP search found glycerophosphocholine and pentosidine as the most significant discriminatory metabolites. Uracil, glutamine, and butyrylcarnitine were also among the significant metabolites. : Metabolomics biomarkers and an explainable AI (XAI)-based prediction model showed significant diagnostic accuracy and sensitivity in the detection of BC. The proposed XAI system using interpretable metabolite data can serve as a clinical decision support tool to improve early diagnosis processes.

Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Cancer Detection.

: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by joint inflammation and pain. Metabolomics approaches, which are high-throughput profiling of small molecule metabolites in plasma or serum in RA patients, have so far provided biomarker discovery in the literature for clinical subgroups, risk factors, and predictors of treatment response using classical statistical approaches or machine learning models. Despite these recent developments, an explainable artificial intelligence (XAI)-based methodology has not been used to identify RA metabolomic biomarkers and distinguish patients with RA. This study constructed a XAI-based EBM model using global plasma metabolomics profiling to identify metabolites predictive of RA patients and to develop a classification model that can distinguish RA patients from healthy controls. : Global plasma metabolomics data were analysed from RA patients (49 samples) and healthy individuals (10 samples). SMOTE technique was used for class imbalance in data preprocessing. EBM, LightGBM, and AdaBoost algorithms were applied to generate a discriminatory model between RA and controls. Comprehensive performance metrics were calculated, and the interpretability of the optimal model was assessed using global and local feature descriptions. : A total of 59 samples were analysed, 49 from RA patients, and 10 from healthy subjects. The EBM generated better results than LightGBM and AdaBoost by attaining an AUC of 0.901 (95% CI: 0.847-0.955) with 87.8% sensitivity which helps prevent false negative early RA diagnosis. The primary biomarkers EBM-based XAI identified were -acetyleucine, pyruvic acid, and glycerol-3-phosphate. EBM global explanation analysis indicated that elevated pyruvic acid levels were significantly correlated with RA, whereas -acetyleucine exhibited a nonlinear relationship, implying possible protective effects at specific concentrations. : This study underscores the promise of XAI and evidence-based medicine methodology in developing biomarkers for RA through metabolomics. The discovered metabolites offer significant insights into RA pathophysiology and may function as diagnostic biomarkers or therapeutic targets. Incorporating EBM methodologies integrated with XAI improves model transparency and increases the therapeutic applicability of predictive models for RA diagnosis/management. Furthermore, the transparent structure of the EBM model empowers clinicians to understand and verify the reasoning behind each prediction, thereby fostering trust in AI-assisted decision-making and facilitating the integration of metabolomic insights into routine clinical practice.

Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis.

The rapid integration of artificial intelligence (AI) into healthcare has enhanced diagnostic accuracy; however, patient engagement and satisfaction remain significant challenges that hinder the widespread acceptance and effectiveness of AI-driven clinical tools. This study introduces CareAssist-GPT, a novel AI-assisted diagnostic model designed to improve both diagnostic accuracy and the patient experience through real-time, understandable, and empathetic communication. CareAssist-GPT combines high-resolution X-ray images, real-time physiological vital signs, and clinical notes within a unified predictive framework using deep learning. Feature extraction is performed using convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformer-based NLP modules. Model performance was evaluated in terms of accuracy, precision, recall, specificity, and response time, alongside patient satisfaction through a structured user feedback survey. CareAssist-GPT achieved a diagnostic accuracy of 95.8%, improving by 2.4% over conventional models. It reported high precision (94.3%), recall (93.8%), and specificity (92.7%), with an AUC-ROC of 0.97. The system responded within 500 ms-23.1% faster than existing tools-and achieved a patient satisfaction score of 9.3 out of 10, demonstrating its real-time usability and communicative effectiveness. CareAssist-GPT significantly enhances the diagnostic process by improving accuracy and fostering patient trust through transparent, real-time explanations. These findings position it as a promising patient-centered AI solution capable of transforming healthcare delivery by bridging the gap between advanced diagnostics and human-centered communication.

CareAssist GPT improves patient user experience with a patient centered approach to computer aided diagnosis.

Alzheimer's disease (AD) is commonly defined by a progressive decline in cognitive functions and memory. Early detection is crucial to mitigate the devastating impacts of AD, which can significantly impair a person's quality of life. Traditional methods for diagnosing AD, while still in use, often involve time-consuming processes that are prone to errors and inefficiencies. These manual techniques are limited in their ability to handle the vast amount of data associated with the disease, leading to slower diagnosis and potential misclassification. Advancements in artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), offer promising solutions to these challenges. AI techniques can process large datasets with high accuracy, significantly improving the speed and precision of AD detection. However, despite these advancements, issues such as limited accuracy, computational complexity, and the risk of overfitting still pose challenges in the field of AD classification. To address these challenges, the proposed study integrates deep learning architectures, particularly ResNet101 and long short-term memory (LSTM) networks, to enhance both feature extraction and classification of AD. The ResNet101 model is augmented with innovative layers such as the pattern descriptor parsing operation (PDPO) and the detection convolutional kernel layer (DCK), which are designed to extract the most relevant features from datasets such as ADNI and OASIS. These features are then processed through the LSTM model, which classifies individuals into categories such as cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD). Another key aspect of the research is the use of generative adversarial networks (GANs) to identify the progressive or non-progressive nature of AD. By employing both a generator and a discriminator, the GAN model detects whether the AD state is advancing. If the original and predicted classes align, AD is deemed non-progressive; if they differ, the disease is progressing. This innovative approach provides a nuanced view of AD, which could lead to more precise and personalized treatment plans. The numerical outcome obtained by the proposed model for ADNI dataset is 0.9931, and for OASIS dataset, the accuracy gained by the model is 0.9985. Ultimately, this research aims to offer significant contributions to the medical field, helping healthcare professionals diagnose AD more accurately and efficiently, thus improving patient outcomes. Furthermore, brain simulation models are integrated into this framework to provide deeper insights into the underlying neural mechanisms of AD. These brain simulation models help visualize and predict how AD may evolve in different regions of the brain, enhancing both diagnosis and treatment planning.

GAN-enhanced deep learning for improved Alzheimer's disease classification and longitudinal brain change analysis.

Accurate and timely segmentation of ischemic stroke lesions from diffusion-weighted imaging (DWI) is crucial for diagnosis and treatment planning. Manual segmentation is labor-intensive, time-consuming, and prone to inter-observer variability. This study aims to develop and validate a novel deep learning framework that overcomes the common trade-off between high segmentation accuracy and the computational efficiency required for practical clinical use.

FA-UNet: A FasterNet and Attention-Gated Hybrid Network for Precise Ischemic Stroke Segmentation.

Brain tumors represent a significant health challenge in India, with approximately 28,000 new cases diagnosed annually. Conventional deep learning approaches for MRI-based segmentation often struggle with irregular tumor boundaries, heterogeneous intensity patterns, and complex spatial relationships, resulting in limited clinical interpretability despite high numerical accuracy. This study introduces CausalX-Net, a causality-guided explainable segmentation network for brain tumor analysis from multi-modal MRI. Unlike purely correlation-based models, CausalX-Net leverages structural causal modeling and interventional reasoning to identify and quantify the causal influence of imaging features and spatial regions on segmentation outcomes. Through counterfactual analysis, the framework can provide clinically relevant "what-if" explanations, such as predicting changes in tumor classification if specific modalities, regions, or features are altered. Evaluated on the BraTS 2021 dataset, CausalX-Net achieved a Dice Similarity Coefficient of 92.5%, outperforming state-of-the-art CNN-based baselines by 4.3% while maintaining competitive inference efficiency. Furthermore, causal attribution maps and intervention-based sensitivity analyses enhance trust and transparency, offering radiologists actionable insights for diagnosis and treatment planning. This research demonstrates that integrating causal inference into segmentation not only improves accuracy but also delivers interpretable, decision-supportive explanations, representing a significant step toward transparent and reliable AI-assisted neuroimaging in clinical settings.

CausalX-Net: a causality-guided explainable segmentation network for brain tumors.

Brain stroke is still one of the leading causes of death and long-term disability in the world. Early and correct diagnosis is therefore important for patient outcome. Although Convolution Neural Network (CNN), classical machine learning models, have achieved great progress in medical image classification, they have to face the performance saturation problem when dealing with high-dimensional and complex data such as medical images. To tackle these limitations, we propose QBrainNet, a quantum enhanced model, which is to enhance brain stroke prediction from medical imaging datasets.

QBrainNet: harnessing enhanced quantum intelligence for advanced brain stroke prediction from medical imaging.

Approximate computing comes to the fore as an alternative paradigm to enhance efficiency in computing systems by trading off the system's accuracy for better performance. This paper seeks to leverage the principles of approximate computing to design efficient multiplier architectures for FPGA platforms. Specifically, this work presents FPGA implementations of one accurate and two approximate multiplier units based on the Dadda algorithm. The multipliers employ a novel partial product reduction technique that minimizes the utilized resources and the critical path delay, offering a more resource-efficient alternative than traditional multipliers. Our accurate and best-performing approximate 8 × 8 multiplier shows an improvement of 28% and 37% in PDAP over the Xilinx exact multiplier and the most performance-efficient existing approximate multiplier, respectively. Further evaluation based on the processing of images with different modalities shows a substantial improvement in PSNR over the existing approximate multipliers, especially in the healthcare domain, thereby highlighting the possible application of the proposed multipliers in error-resilient medical imaging tasks.

ArticleTotal : 2	Year
Publication title	2026
Publication title	2026

Article	Year
Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence. Medicina (Kaunas, Lithuania)\| PubMed ID: 40142216	2025
Deep learning for cerebral vascular occlusion segmentation: A novel ConvNeXtV2 and GRN-integrated U-Net framework for diffusion-weighted imaging. Neuroscience\| PubMed ID: 40204150	2025
Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Cancer Detecti... Medicina (Kaunas, Lithuania)\| PubMed ID: 40282875	2025
Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis. Medicina (Kaunas, Lithuania)\| PubMed ID: 40428791	2025
CareAssist GPT improves patient user experience with a patient centered approach to computer aided diagnosis. Scientific reports\| PubMed ID: 40594036	2025
GAN-enhanced deep learning for improved Alzheimer's disease classification and longitudinal brain change analysis. Frontiers in medicine\| PubMed ID: 40600033	2025
FA-UNet: A FasterNet and Attention-Gated Hybrid Network for Precise Ischemic Stroke Segmentation. Journal of integrative neuroscience\| PubMed ID: 41200985	2025
CausalX-Net: a causality-guided explainable segmentation network for brain tumors. Frontiers in medicine\| PubMed ID: 41210853	2025
QBrainNet: harnessing enhanced quantum intelligence for advanced brain stroke prediction from medical imaging. Frontiers in medicine\| PubMed ID: 41210857	2025
Design of high-performance, accurate, and approximate Dadda-tree multipliers for image processing applications. Scientific reports\| PubMed ID: 41272023	2025

Ali Algarni

Biography

JoVE Journal Publications

Other Publications