Analysing DCE-MRI scans using hybrid techniques for early detection of prostate cancer based on fusion features of handcrafted and deep learning features Ali M. Hasan, Wallaa L. Alfalluji, Mohammed A. Hamdawi, Hamid A. Jalab, Rabha W. Ibrahim, Farid Meziane Journal of Medical Engineering and Technology, 2026 Prostate cancer is among the most diagnosed malignancies in men worldwide and a leading cause of cancer-related mortality. Early and accurate diagnosis is critical to improve patient outcomes and reduce the risks of overtreatment or missed detection. Conventional diagnostic approaches, including prostate-specific antigen (PSA) testing, digital rectal examination (DRE), and histopathological analysis, often suffer from limited sensitivity and specificity, leading to false positive or delayed diagnosis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has recently emerged as an effective modality for prostate cancer detection, providing complementary anatomical and functional information. This study proposes a novel hybrid diagnostic framework that integrates Generalized Quantum Gamma Polynomial (GQGP) features, kinetic signal intensity features, and deep learning-based representations. GQGP features capture subtle intensity variations and quantum-inspired statistical characteristics, while kinetic features quantify contrast-enhancement dynamics to discriminate malignant from benign tissues. These handcrafted descriptors are fused with high-level features extracted using convolutional neural networks (CNNs) to construct a comprehensive feature representation. Experimental evaluation on publicly available prostate imaging datasets demonstrates that the proposed fusion framework significantly outperforms single-feature and traditional methods, achieving a classification accuracy of 97.32%. The results highlight the effectiveness of combining mathematical modeling, radiomics, and artificial intelligence for improved prostate cancer diagnosis.
Advancements in Cancer Detection: An Artificial Intelligence-Based Approach Using PET/CT Datasets Faten Imad Ali, Hadeel K. AlJobouri, Ali M. Hasan Al Nahrain Journal for Engineering Sciences, 2025 Artificial intelligence (AI) is rapidly advancing as a valuable tool in oncology for enhancing detection and management of cancer. The integration of AI with PET/CT imaging presents significant scenarios for improving efficiency and accuracy of cancer diagnosis. This study examines the current applications of AI with PET/CT imaging, highlighting its role in diagnosing, differentiating, delineating, staging, assessing therapy response, determining prognosis, and enhancing image quality. A comprehensive literature search was conducted in six data-bases to get the most recent works, use Springer, Scopus, PubMed, Web of Science, IEEE, and Google Scholar in the last five years (2019-2024), identifying 80 studies that met the criteria for inclusion that focused on AI-driven models applied to PET/CT data in various cancers, with lung cancer being the most studied. Other cancers examined include head and neck, breast, lymph nodes, whole body, and others. All studies involved human subjects. The findings indicate that AI holds promise in improving cancer detection, identifying benign from malignant tumors, aiding in segmentation, response evaluation, staging, and determining the prognosis. However, the application of AI-powered models and PET/CT-derived radiomics in clinical practice is limited because of issues of data normalization, reproducibility, and the requirement of large multi-center data sets for improving model generalizability. All these limitations have to be solved to guarantee the dependable and ethical use of AI in day-to-day clinical activities.
Skin Lesion Detection Using Handcrafted and DL-Based Features Fusion Hawraa Riyad Hamza, Hussain S. Hasan, Ali M. Hasan Csase 2025 International Conference on Computer Science and Software Engineering, 2025 Skin cancer is a common cancer, with melanoma being the most dangerous type. Early diagnosis is crucial for effective treatment and better outcomes. Dermatologists have widely used digital dermoscopy for cancer diagnosis, although it continues to rely substantially on the clinician's experience and knowledge. Automated skin lesion categorization systems are a promising approach to helping dermatologists make accurate diagnoses. This study is focused on creating a novel approach for distinguishing between benign and malignant tumors in dermoscopic skin images. The procedure starts with image preprocessing techniques, including enhancement, normalization, segmentation, and zero-padding. Then, the extracted features were implemented by fusing convolutional neural networks and gray-level co-occurrence matrix features. Finally, a support vector machine model classifies the extracted features as benign or malignant. A dataset of 900 dermoscopic images of the skin was classified with 97.1% accuracy. The area under the curve was 98.1%. Combining gray-level co-occurrence matrix and convolutional neural network features with a large publicly available dataset improves detection accuracy for skin cancer.
An Artificial Intelligence Techniques in Lung Cancer 18F-Fluorodeoxyglucose Radiotracer PET/CT Imaging Faten Imad Ali, Hadeel K. AlJobouri, Ali M. Hasan, Alwahab Dhulfiqar Zoltan Procedia Computer Science, 2025 Lung cancer is one of the most prevalent and deadly diseases in the world; thus, early identification is essential for increased survival rates. PET/CT scans with an 18 F-FDG radiotracer are crucial tools in the diagnosis of cancer, as they combine two imaging modalities to provide a more complete view than each alone. PET/CT scans don’t always provide the whole picture, though, because tumors are complicated and image interpretations can differ. AI can help with this. AI increases accuracy and lessens the workload for physicians by automating diagnosis and classification. These advanced algorithms, trained to match expert knowledge, not only detect key symptoms but also enhance image quality, leading to more precise diagnoses. This study examines the advancements in applying AI- algorithms to 18 F-FDG-PET/CT imaging for lung cancer, to enhance clinical outcomes. We conducted a comprehensive search on several databases to get the most recent works, using Springer, Scopus, PubMed, Web of Science, IEEE, and Google Scholar in the last five years (2019-2024). Identifying studies that met the criteria for inclusion that focused on AI-driven models applied to PET/CT data in lung cancers. The findings highlight the varying performance of AI models in lung cancer analysis using 18 F-FDG-PET/CT imaging, with deep learning models like Convolutional Neural Networks (CNNs) and Residual Neural Network (ResNet) achieving higher accuracy (The area under the ROC curve AUC 98-99%) compared to traditional machine learning approaches (AUC 63%). Challenges such as small sample sizes, lack of external validation, and computational complexity hinder broader clinical adoption. Furthermore, imaging quality, data consistency, and model interpretability limit effectiveness. The conclusion how AI has the potential to revolutionize lung cancer diagnosis and treatment. However, it also underscores the importance of continued research to tackle challenges like ensuring the models work well across different settings, making their decisions easier to understand, and adapting them for practical, real-world use.
Hybrid Pre-Trained Models with Machine Learning Classifier for Skin Cancer Classification Toqa A. Sadoon, Asaad F. Qasim, Mohammed Khalaf, Ali M. Hasan Baghdad Science Journal, 2025 Classification of skin lesions can be challenging because of the subtle variation on the skin surface due to the appearance of these lesions. In the other hand, the digital dermoscopy has been widely used by dermatologist to diagnose cancer. For accurate detection, the clinicians should have a lot of experiences, but human nature is prone to error, forgetfulness, tension, and speed in diagnosis, all of which can affect the accuracy of detection. For these reasons, automated skin lesion classification has been used by many researchers to help dermatologists in making the right and accurate decision during diagnosis. A preprocessing image pipeline has been applied in this work prior to classification, including image enhancement, image normalization, and resizing. By established transfer learning on ImageNet weights of pre-trained and fine-tuning the model to meet our purpose. Five pre-trained models of CNN are used with four type of machine learning classifiers on HAM10000 dataset. This paper suggests a hybrid model for feature extraction, which then feeds these features to the classifier to classify dermoscopy images as either benign or malignant. The highest reported score by concatenated DenseNet201 and Mobil Net with SVM classifier are 87.8%, 86.956%, 87.912%, 87.755%, 87.43%, 100%, 94%, and 90% in term of Precision, Sensitivity, Specificity, F1-score, AUC for training data, AUC for validation data, and AUC for testing data, respectively.
Breast Masses in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Classification Based on Combining Deep Learning and Local Binary Pattern Features Rabab Kadhim, Hussain Hasan, Ali Hasan Icsintesa 2024 2024 4th International Conference of Science and Information Technology in Smart Administration the Collaboration of Smart Technology and Good Governance for Sustainable Development Goals, 2024 The project’s objective is to create a novel method for separating benign from malignant tumors in breast DCE-MRI scans. The proposed local binary pattern (LBP), kinetic curve, and convolutional neural network (CNN) techniques were used to extract textural information from magnetic resonance imaging (MRI). After that, the traits were categorized as benign or malignant using the support vector machines (SVMs) model. The achieved classification accuracy was 97.3%.
Automated segmentation of tumours in MRI brain scans Bioimaging 2016 3rd International Conference on Bioimaging Proceedings Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies Biostec 2016, 2016
Breast Tumor Classification in DCE-MRI Using Hybrid Feature Extraction Techniques AI Shamkhi, HK Aljobouri, A Hasan International Conference on Breakthroughs in Artificial Intelligence, 215-228 , 2026 2026
Analysing DCE-MRI scans using hybrid techniques for early detection of prostate cancer based on fusion features of handcrafted and deep learning features AM Hasan, WL Alfalluji, MA Hamdawi, HA Jalab, RW Ibrahim, F Meziane Journal of Medical Engineering & Technology, 1-13 , 2026 2026
Peritonitis rates among Automated peritoneal dialysis patients: Underlying factors and outcomes. M Shakir, M Anees, I Munir, H Akbar, M Mahmood, S Pervaiz, I Elahi, ... Pakistan Journal of Kidney Diseases 9 (4), 3-6 , 2025 2025
Advancements in Cancer Detection: An Artificial Intelligence-Based Approach Using PET/CT Datasets FI Ali, HK AlJobouri, AM Hasan Al-Nahrain Journal for Engineering Sciences 28 (3), 451-460 , 2025 2025
Molecular subtypes classification of breast cancer in DCE-MRI using deep features (vol 236, 121371, 2024) AM Hasan, NKN Al-Waely, HK Aljobouri, HA Jalab, RW Ibrahim, ... EXPERT SYSTEMS WITH APPLICATIONS 270 , 2025 2025
Corrigendum to “Molecular subtypes classification of breast cancer in DCE-MRI using deep features”[Expert Syst. Appl. 236 (2024) 121371] AM Hasan, NKN Al-Waely, HK Aljobouri, HA Jalab, RW Ibrahim, ... 2025
Skin Lesion Detection Using Handcrafted and DL-Based Features Fusion HR Hamza, HS Hasan, AM Hasan 2025 International Conference on Computer Science and Software Engineering … , 2025 2025
Discrimination of invasive ductal and lobular carcinoma of the breast based on the combination of enhanced Legendre polynomial, kinetic features and deep learning features FM Ali M. Hasan, Noor K.N. Al-Waely, Hadeel K. Aljobouri, Hamid A. Jalab ... Biomedical Signal Processing and Control 104 (June 2025), 107546 , 2025 2025 Citations: 4
Hybrid Pre-Trained Models with Machine Learning Classifier for Skin Cancer Classification TA Sadoon, AF Qasim, M Khalaf, AM Hasan Baghdad Science Journal 22 (12), 4228-4240 , 2025 2025
An Artificial Intelligence Techniques in Lung Cancer 18F-Fluorodeoxyglucose Radiotracer PET/CT Imaging FI Ali, HK AlJobouri, AM Hasan, AD Zoltan Procedia Computer Science 259, 202-208 , 2025 2025 Citations: 3
Breast Masses in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Classification Based on Combining Deep Learning and Local Binary Pattern Features R Kadhim, H Hasan, A Hasan 2024 4th International Conference of Science and Information Technology in … , 2024 2024 Citations: 2
Molecular subtypes classification of breast cancer in DCE-MRI using deep features AM Hasan, NKN Al-Waely, HK Aljobouri, HA Jalab, RW Ibrahim, ... Expert systems with applications 236, 121371 , 2024 2024 Citations: 29
Lung CT image segmentation using VGG-16 network with image enhancement based on bounded turning mittag-leffler function A M Hasan, M Khalaf, B M Sabbar, R W Ibrahim, H A Jalab, F Meziane Baghdad Science Journal 21 (12), 10 , 2024 2024 Citations: 1
Diagnosis of breast cancer based on hybrid features extraction in dynamic contrast enhanced magnetic resonance imaging AM Hasan, HK Aljobouri, NKN Al-Waely, RW Ibrahim, HA Jalab, ... Neural Computing and Applications 35 (31), 23199-23212 , 2023 2023 Citations: 5
A classification model of breast masses in DCE-MRI using kinetic curves features with quantum-Raina’s polynomial based fusion AM Hasan, NKN Al-Waely, HK Ajobouri, RW Ibrahim, HA Jalab, ... Biomedical Signal Processing and Control 84, 105002 , 2023 2023 Citations: 18
MRI Brain Scans Classification Using Extreme Learning Machine on LBP and GLCM. JYR Al-Awadi, HK Aljobouri, AM Hasan International Journal of Online & Biomedical Engineering 19 (2) , 2023 2023 Citations: 17
Breast cancer MRI classification based on fractional entropy image enhancement and deep feature extraction AM Hasan, AF Qasim, HA Jalab, RW Ibrahim Baghdad Science Journal 20 (1), 17 , 2023 2023 Citations: 22
MRI brain tumor classification using robust Convolutional Neural Network CNN approach JY Rbat, HK Aljobouri, AM Hasan 2022 Iraqi International Conference on Communication and Information … , 2022 2022 Citations: 2
Chest CT Images Analysis with Deep-Learning and Handcrafted Based Algorithms for COVID-19 Diagnosis JF Abdulkareem, HK Aljobouri, AM Hasan Des Eng, 6246-6262 , 2021 2021 Citations: 2
Magnetic resonance imaging breast scan classification based on texture features and long short-term memory model SR Hilal, HS Hasan, AM Hasan NeuroQuantology 19 (7), 41-47 , 2021 2021 Citations: 11
MOST CITED SCHOLAR PUBLICATIONS
Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features AM Hasan, MM Al-Jawad, HA Jalab, H Shaiba, RW Ibrahim, ... Entropy 22 (5), 517 , 2020 2020 Citations: 183
Combining Deep and Handcrafted Image Features for MRI Brain Scan Classification AM Hasan, HA Jalab, F Meziane, H Kahtan, AS Al-Ahmad IEEE Access , 2019 2019 Citations: 137
Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge AM Hasan, F Meziane, R Aspin, HA Jalab Symmetry 8 (11), 132 , 2016 2016 Citations: 89
A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans RW Ibrahim, AM Hasan, HA Jalab Computer methods and programs in biomedicine 163, 21-28 , 2018 2018 Citations: 47
Automated screening of MRI brain scanning using grey level statistics FM Ali M. Hasan Computers and Electrical Engineering 53 , 2016 2016 Citations: 45
Magnetic Resonance Imaging Segmentation Techniques of Brain Tumors: A Review H Jalab, AM Hasan Archives of Neuroscience , 2019 2019 Citations: 40
MRI brain classification using the quantum entropy LBP and deep-learning-based features AM Hasan, HA Jalab, RW Ibrahim, F Meziane, AR AL-Shamasneh, ... Entropy 22 (9), 1033 , 2020 2020 Citations: 30
Molecular subtypes classification of breast cancer in DCE-MRI using deep features AM Hasan, NKN Al-Waely, HK Aljobouri, HA Jalab, RW Ibrahim, ... Expert systems with applications 236, 121371 , 2024 2024 Citations: 29
Breast cancer MRI classification based on fractional entropy image enhancement and deep feature extraction AM Hasan, AF Qasim, HA Jalab, RW Ibrahim Baghdad Science Journal 20 (1), 17 , 2023 2023 Citations: 22
A new medical image enhancement algorithm based on fractional calculus HA Jalab, RW Ibrahim, AM Hasan, FK Karim, AR Al-Shamasneh, ... Computers, Materials, & Continua 68 (2), 1467 , 2021 2021 Citations: 21
Performance of Grey Level Statistic Features versus Gabor wavelet for Screening MRI Brain Tumors: A Comparative Study AM Hasan, F Meziane, HA Jalab The 6th International Conference on Information Communication and Management … , 2016 2016 Citations: 21
Automated segmentation of tumours in mri brain scans AM Hasan, F Meziane, M Khadim Proceedings of the 9th International Joint Conference on Biomedical … , 2016 2016 Citations: 21
A hybrid approach of using particle swarm optimization and volumetric active contour without edge for segmenting brain tumors in MRI scan AM Hasan Indonesian Journal of Electrical Engineering and Informatics (IJEEI) 6 (3 … , 2018 2018 Citations: 20
A classification model of breast masses in DCE-MRI using kinetic curves features with quantum-Raina’s polynomial based fusion AM Hasan, NKN Al-Waely, HK Ajobouri, RW Ibrahim, HA Jalab, ... Biomedical Signal Processing and Control 84, 105002 , 2023 2023 Citations: 18
MRI Brain Scans Classification Using Extreme Learning Machine on LBP and GLCM. JYR Al-Awadi, HK Aljobouri, AM Hasan International Journal of Online & Biomedical Engineering 19 (2) , 2023 2023 Citations: 17
Combination of Local Binary Pattern and Face Geometric Features for Gender Classification from Face Images HK Omer, HA Jalab, AM Hasan, NE Tawfiq 2019 9th IEEE International Conference on Control System, Computing and … , 2020 2020 Citations: 17
Magnetic resonance imaging breast scan classification based on texture features and long short-term memory model SR Hilal, HS Hasan, AM Hasan NeuroQuantology 19 (7), 41-47 , 2021 2021 Citations: 11
MRI brain scan classification using novel 3-D statistical features AM Hasan, F Meziane, R Aspin, HA Jalab 2nd International Conference on Internet of Things, Data and Cloud Computing … , 2017 2017 Citations: 11
Medical Image Enhancement Based on Statistical Distributions in Fractional Calculus HA Jalab, RW Ibrahim, AM Hasan Computing Conference, 18-20 July 2017 | London, UK , 2017 2017 Citations: 8
MRI brain scans classification using bi-directional modified gray level co-occurrence matrix and long short-term memory AM Hasan, M.H. , Hasan, H.S. , Hasan NeuroQuantology 18 (9), 54-63 , 2020 2020 Citations: 6
