Deep Learning-Based Detection of Renal Abnormalities from CT Images: A Study on a Bangladeshi Cohort

First steps in clinical implementation of a lung nodule classification method by artificial intelligence

This work represents an initial translational step toward real-world clinical implementation of AI-based lung nodule classification at Cheikh Zaid Hospital and highlights the feasibility of integrating AI tools into routine radiology workflows.

Explainable deep learning models for kidney stone classification using axial CT scans

Chronic renal disease classification using the AL-5-ENeT-B4 model from CT images.

Chronic Renal Disease (CRD) is an increasing global health burden, largely driven by the rising prevalence of diabetes and hypertension. Early and accurate identification of renal abnormalities from computed tomography (CT) images is clinically important, yet manual interpretation is time-consuming and conventional machine learning methods often have limited scalability for complex multiclass medical image classification. This study proposes AL-5-ENeT-B4, a modified EfficientNet-B4 architecture enhanc d with five additional layers for automated CRD classification from CT images. The proposed framework is integrated an image preprocessing, data augmentation, stratified 5-fold cross-validation, and deep learning of pre-trained features to classify renal CT scans into one of the four categories: cyst, normal, stone, or tumour. The results of this model were evaluated using 5-fold cross-validation. The proposed model achieved an average K-fold accuracy (99.18%), precision (99.18%), and recall score (99.19%), with an F1-score (99.18%). Furthermore, Grad-CAM visualisation used to provide better interpretability by highlights those renal areas that had clinical significance in relation to the model's decisions. The proposed AL-5-ENeT-B4 model is compared with ResNet50, VGG16, DenseNet121, and ViT-B/16 and demonstrated significantly improved performance achieving an average MCC of 0.9912 and Cohen's Kappa of 0.9912(p < 0.001).

CT Imaging-based Ensemble Deep Learning Model for Lung Cancer Detection

Lung cancer is the main cause of death related to cancer globally, taking the lives of about 1.8 million people each year. Detecting it early and accurately from CT scan images is very important for better patient results. Three sophisticated Convolutional Neural Network (CNN) models—ResNet50, DenseNet121, and EfficientNetB0—are used in this comprehensive study to automatically classify lung CT scan images into two categories (benign and malignant). The IQ-OTH/NCCD Lung Cancer Dataset, which is openly accessible on Kaggle, was used to train and evaluate the models. This dataset was created by Aditya Mahimkar and contains 1300 CT scan slices from 110 different patient cases. After preprocessing, only benign and malignant CT scan images were used for binary classification. To evaluate the performance of each model following training through transfer learning and fine-tuning, metrics such as accuracy, precision, recall, F1-score, and the confusion matrix were employed. ResNet50 outperformed DenseNet121 and EfficientNetB0 in terms of accuracy. A combined Weighted Ensemble Model that uses the probability outputs from all three networks was also created, which improved the overall classification performance and how well the model works in different situations. This study shows that using deep learning and combining models can be very helpful tools in detecting lung cancer during clinical screenings.

Deep learning driven early detection of lung cancer from CT images using transfer learning approaches

The proposed system demonstrates potential as an assistive tool for automated lung cancer screening, warranting further validation on larger and multi-institutional datasets before clinical application.

Comparative Performance Analysis of Deep Learning Architectures for Oral Cancer Detection Using Histopathological Images

Early and accurate detection of oral cancer is critical for improving patient outcomes. This study presents a comprehensive comparison of five convolutional neural network architectures—MobileNetV2, Xception, VGG19, ResNet50, and DenseNet201—for automated oral cancer detection from histopathological images. All models were fine-tuned through transfer learning using ImageNet pretrained weights and evaluated on 5,192 histopathological images from Kaggle. On this dataset, MobileNetV2 achieved highest classification accuracy $(\mathbf{9 0. 0 0 \%})$, followed by DenseNet201 (87.69%), VGG19 (86.54%), Xception (86.15%), and ResNet50 (83.27%). Statistical validation via paired t-tests confirmed significant performance differences $(p<0.05)$. Beyond accuracy, MobileNetV2 demonstrated 38% faster training, 84% smaller model size, and 33% faster inference, showing promise for resource-constrained clinical settings pending external validation.