Dementia Classification Based on Magnetic Resonance Scans Comparing Traditional and Modern Machine Learning Models’ Quintessence

Dementia is a neurodegenerative condition that affects many people globally, and early diagnosis is essential to slow down its progression. This paper aimed to analyze and compare several machine learning models used for the classification of Magnetic Resonance Imaging (MRI) scans of patients with or without dementia. To achieve this, we utilized multiple open-source datasets, such as the Alzheimer MRI Disease Classification Dataset and the Augmented Alzheimer MRI Dataset. The tested models include a few-layers Convolutional Neural Network (CNN), a Residual Neural Network (ResNet), a Vision Transformer (ViT), an Autoencoder, and the Random Forest actions, and their training was conducted in Google Colab on an Intel Xeon CPU with 2 vCPUs and above 10 GB RAM. The performance evaluation of each model was based on metrics such as accuracy, sensitivity, specificity, and statistical errors (determination coefficient, mean squared error, root mean squared error). Preliminary results indicate that ResNet achieves the highest accuracy (98.98%), followed by few-layers CNN (94%) and Random Forest (91%). ViT showed variable performance depending on the dataset, ranging between 48.83% and 99.64%, while the Autoencoder had lower classification performance (71.64% - 89%), being more suitable for data preprocessing. Deep neural network-based models, ResNet and ViT, demonstrated the best classification capabilities for individual datasets. Parameter optimization and data adaptability to requirements could further improve the obtained performance

Data Augmentation and Lightweight Convolutional Neural Networks in Classification of Thoracic Diseases

The medical field has seen a tremendous transformation due to technological breakthroughs, with advanced medical imaging techniques becoming indispensable for diagnosis and treatment. Convolutional neural network (CNN) models have demonstrated remarkable accuracy in analyzing and classifying medical images, often surpassing human performance. In this study, we contrast two important methods for classifying medical images: lightweight CNN models (that are tailored for devices with limited resources) and data augmentation (to improve model generalization). Evaluating these models' effectiveness and performance in identifying thoracic illnesses, such as breast cancer, COVID-19 effects, and pneumonia, is the main goal. To enhance model performance, the study used preprocessed and augmented publicly available chest X-ray scans and computer tomography images. Specific CNN models used in the experiments are MobileNet, EfficientNet-B0, ResNet50, and DenseNet121. The state-of-the-art for these models show that despite lowering the danger of overfitting, data augmentation greatly increases model accuracy. Lightweight models provided the best possible compromise between accuracy and resource efficiency, performing on par with complicated models. The suitability of lightweight models for portable medical equipment was validated by testing on devices with limited resources, allowing for quick and precise pre-diagnosis. In addition to highlighting the potential of lightweight CNN to increase diagnostic accessibility, these findings emphasize the importance of striking balancing performance and efficiency in medical applications, particularly in resource-limited settings

Integrated Platform for Bio-Image Processing and Identification of Cervical Cancer

Cervical cancer is a major global health issue, ranking among the leading causes of death in women. Early detection is essential in reducing mortality rates, but traditional diagnostic methods, such as manual examination of cytological and histological images, are time-consuming and prone to human error. To address these limitations, our study presents a comprehensive platform for bio-image processing and early diagnosis of cervical cancer, employing advanced machine learning and deep learning techniques. The proposed methodology consists of several stages, starting with the preprocessing of medical images, including normalization, augmentation, and noise reduction. Following this, the cervical cells are segmented and classified using a combination of machine learning algorithms, such as shallow machine learning models and Convolutional Neural Networks (CNN). We selected publicly available datasets, including Herlev and SIPaKMeD, to evaluate the performance of the proposed models, which are a very respectful base in the state-of-the-art analysis. Considering convex models and papers, CNN showed the best results on augmented cervical cells data. In comparison, traditional machine learning algorithms, concretely K-Nearest Neighbors and Naive Bayes, produced lower results, emphasizing the advantages of deep learning models in medical image analysis. The integration of artificial intelligence in medical imaging can contribute to early detection and contribute to reduction of mortality rates associated with this disease. A future direction is to explore the applications of Vision Transformers, which have demonstrated excellent performance in capturing complex patterns in image data, to further improve data preprocessing and classification accuracy

Deep Learning-Based Denoising for Optical Coherence Tomography: Evaluating Self-Supervised and Generative Models Across Retinal Datasets

Denoising medical imaging is crucial for enhancing diagnostic accuracy, particularly for Optical Coherence Tomography (OCT) scans used to detect retinal diseases. We aimed to evaluate the performance of five deep learning-based denoising models, namely Zero Shot Noise2Noise (ZS-N2N), DnCNN (for Gaussian denoising), U-Net Autoencoder, SwinIR Transformer, and CycleGAN. We used OCT scans with different retinal diseases datasets, such as diabetic retinopathy, age-related macular degeneration, macular hole, central serous retinopathy, and normal retinas. The models were trained using diverse OCT images and tested across these datasets to assess their generalization capability. Preliminary results indicated that ZS-N2N and CycleGAN consistently achieve the lowest loss and highest accuracy, making them the most effective for denoising across different pathologies. The DnCNN and U-Net Autoencoder exhibited moderate performance, with slightly higher loss values, likely due to their sensitivity to fine structural variations. SwinIR Transformer performs comparably to convolutional-based models but slightly underperforms on structurally complex conditions such as macular holes and central serous retinopathy. The accuracy values suggest that normal retina images achieve the highest denoising performance (approximatively 96% for ZS-N2N and CycleGAN). Overall, the results of our study highlights the effectiveness of self-supervised and generative adversarial approaches in preserving essential medical details while removing noise. Future work will involve refining these models with domain-specific augmentations and validating results on larger datasets

A Data-Driven Machine Learning Framework Proposal for Selecting Project Management Research Methodologies

Selecting appropriate research methodologies in project management traditionally relies on individual expertise and intuition, leading to variability in study design and reproducibility challenges. To address this gap, we introduce a machine learning-driven recommendation system that objectively matches project management use cases to suitable research methods. Leveraging a curated dataset of 156 instances extracted from over 100 peer-reviewed articles, each example is annotated by one of five application domains (cost estimation, performance analysis, risk assessment, prediction, comparison) and one of seven methodology classes (e.g., regression analysis, time-series analysis, case study). We transformed textual descriptions into TF-IDF features and one-hot-encoded contextual domains, then trained and rigorously tuned three classifiers—random forest, support vector machine, and K-nearest neighbours—using stratified five-fold cross-validation. The random forest model achieved superior performance (93.8% ± 1.9% accuracy, macro-F1 = 0.93, ROC-AUC = 0.94), demonstrating robust generalisability across diverse scenarios, while SVM offered the highest precision on dominant classes. Our framework establishes a transparent, reproducible workflow—from literature extraction and annotation to model evaluation—and promises to standardise methodology selection, enhancing consistency and rigour in project management research design