This paper introduces an advanced method for lung cancer subtype classification and detection using the latest version of YOLO, tailored for the analysis of CT images. Given the increasing mortality rates associated with lung cancer, early and accurate diagnosis is crucial for effective treatment planning. The proposed method employs single-shot object detection to precisely identify and classify various types of lung cancer, including Squamous Cell Carcinoma (SCC), Adenocarcinoma (ADC), and Small Cell Carcinoma (SCLC). A publicly available dataset was utilized to evaluate the performance of YOLOv8. Experimental outcomes underscore the system’s effectiveness, achieving an impressive mean Average Precision (mAP) of 97.1%. The system demonstrates the capability to accurately identify and categorize diverse lung cancer subtypes with a high degree of accuracy. For instance, the YOLOv8 Small model outperforms others with a precision of 96.1% and a detection speed of 0.22 seconds, surpassing other object detection models based on two-stage detection approaches. Building on these results, we further developed a comprehensive TNM classification system. Features extracted from the YOLO backbone were reduced using Principal Component Analysis (PCA) to enhance computational efficiency. These reduced features were then fed into a custom TNMClassifier, a neural network designed to classify the Tumor, Node, and Metastasis (TNM) stages. The TNMClassifier architecture comprises fully connected layers and dropout layers to prevent overfitting, achieving an accuracy of 98% in classifying the TNM stages. Additionally, we tested the YOLOv8 Small model on another dataset, the Lung3 dataset from the Cancer Imaging Archive (TCIA). This testing yielded a recall of 0.91, further validating the model’s effectiveness in accurately identifying lung cancer cases. The integrated system of YOLO for subtype detection and the TNMClassifier for stage classification shows significant potential to assist healthcare professionals in expediting and refining diagnoses, thereby contributing to improved patient health outcomes.

This study proposes a Content-Based Image Medical Retrieval (CBIMR) system for lung cancer subtypes, integrating the TNM staging system. We employed YOLOv8, achieving top performance with a precision of 0.961, recall of 0.945, and mAP at 0.5 of 0.971. The model also provided high accuracy in retrieving similar cases across cancer groups and stages. This approach enhances diagnostic accuracy and interpretability, addressing challenges in current deep-learning algorithms for medical imaging.

Liver disease is a major global health concern. Given the critical role of medical image categorization in fibrosis staging (low, moderate, severe, cirrhotic) and the challenges posed by limited medical image datasets, this paper aims to leverage ultrasound imaging to assess liver margin characteristics at the level of Glisson’s capsule—here referred to as Glisson’s line—to develop a simple, automated model for accurately distinguishing fibrosis stages. The proposed approach combines traditional image processing techniques in a pre-processing stage with machine learning algorithms for classification. The pre-processing phase introduces an attention-focusing mechanism that stretches the gray levels of Glisson’s line while shrinking the intensity levels associated with the liver parenchyma and surrounding tissues. This results in the so-called region of contrast interest (ROCI), where potential classification distractors are minimized. For classification, a convolutional neural network (CNN)-based model is used to process original, rotated, and transformed ultrasound images. To address dataset imbalance and overfitting, a 10-fold cross-validation strategy was implemented. The results demonstrate that, by effectively enhancing the information content of Glisson’s line, different liver fibrosis stages can be accurately distinguished without the need for explicit edge detection, achieving accuracy levels comparable to those reported in the literature. The novelty of this work lies in analyzing the morphology of Glisson’s capsule—obtained through this method—rather than focusing on the liver parenchyma and texture, as is traditionally carried out.

This research focuses on fall detection within elevators using the YOLOv8s architecture. From a technical perspective, the experiment should address the following two aspects: 1. A comparison with other YOLOv8 architectures, such as Nano, Large, or...