Video dataset for the detection of safe and unsafe behaviours in workplaces.

Real-time detection of safe and unsafe behaviours in production facilities is very important to prevent these behaviours before they occur. In this context, this study presents a high-resolution video-based dataset of safe and unsafe behaviours obtained from a closed production facility for use in occupational accident prevention. The dataset was collected from the security cameras of a production facility operating in an organised industrial zone in Eskisehir, Turkey, in November and December 2022, after obtaining the necessary permissions from company officials and employees. A total of 8 behaviour classes, 4 classes of safe and 4 classes of unsafe behaviours, were identified for the dataset and 691 video clips containing these behaviours were obtained. The video clips created for the dataset are in MP4 format at 1920×1080 pixels and 24 frames per second. In the dataset, the safe behaviour classes are Safe Walkway, Authorized Intervention, Closed Panel Cover and Safe Carrying, while the unsafe behaviour classes are Safe Walkway Violation, Unauthorized Intervention, Opened Panel Cover and Carrying Overload with Forklift.

A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans.

Lung cancer is one of the most common cancer types. For the survival of the patient, early detection of lung cancer with the best treatment method is crucial. In this study, we propose a novel computer-aided pipeline on computed tomography (CT) scans for early diagnosis of lung cancer thanks to the classification of benign and malignant nodules. The proposed pipeline is composed of four stages. In preprocessing steps, CT images are enhanced, and lung volumes are extracted from the image with the help of a novel method called lung volume extraction method (LUVEM). The significance of the proposed pipeline is using LUVEM for extracting lung region. In nodule detection stage, candidate nodules are determined according to the circular Hough transform- (CHT-) based method. Then, lung nodules are segmented with self-organizing maps (SOM). In feature computation stage, intensity, shape, texture, energy, and combined features are used for feature extraction, and principal component analysis (PCA) is used for feature reduction step. In the final stage, probabilistic neural network (PNN) classifies benign and malign nodules. According to the experiments performed on our dataset, the proposed pipeline system can classify benign and malign nodules with 95.91% accuracy, 97.42% sensitivity, and 94.24% specificity. Even in cases of small-sized nodules (3-10 mm), the proposed system can determine the nodule type with 94.68% accuracy.

CADBOSS: A computer-aided diagnosis system for whole-body bone scintigraphy scans.

AIMS: The aim of this study is to develop a computer-aided diagnosis system for bone scintigraphy scans. (CADBOSS). CADBOSS can detect metastases with a high success rates. The primary purpose of CADBOSS is as supplementary software to facilitate physician's decision making. MATERIALS AND METHODS: CADBOSS consists of various elements, such as hotspot segmentation, feature extraction/selection and classification. A level set active contour segmentation algorithm was used for the detection of hotspots. Moreover, a novel image gridding method was proposed for feature extraction of metastatic regions. An artificial neural network classifier was used to determine whether metastases were present. Performance evaluation of CADBOSS was performed with the help of an image database which included 130 images. (30 non-metastases and 100 metastases) collected from 60 volunteers. All images were obtained within approximately 3 hours after injecting a small amount of radioactive material 99mTc-MDP into the patients and then carrying out scanning with a gamma camera. The 10-fold cross-validation technique was used for all tests. RESULTS: CADBOSS could correctly identify in 120 out of 130 images. Thus, the accuracy, sensitivity, and specificity of CADBOSS were 92.30%, 94%, and 86.67%, respectively. Moreover, CADBOSS increased physician's success in detecting metastases from 95.38% to 96.9%. CONCLUSIONS: Detailed experiments showed that CADBOSS outperforms state-of-the-art computer-aided diagnosis. (CAD) systems and reasonably improves physician' diagnostic success.

Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images.

AIM: The aim was to develop a high-performance computer-aided diagnosis (CAD) system with image processing and pattern recognition in diagnosing pancreatic cancer by using endosonography images. MATERIALS AND METHODS: On the images, regions of interest (ROI) of three groups of patients (<40, 40-60 and >60) were extracted by experts; features were obtained from images using three different techniques and were trained separately for each age group with an Artificial Neural Network (ANN) to diagnose cancer. The study was conducted on endosonography images of 202 patients with pancreatic cancer and 130 noncancer patients. RESULTS: 122 features were identified from the 332 endosonography images obtained in the study, and the 20 most appropriate features were selected by using the relief method. Images classified under three age groups (in years; <40, 40-60 and >60) were tested via 200 random tests and the following ratios were obtained in the classification: accuracy: 92%, 88.5%, and 91.7%, respectively; sensitivity: 87.5%, 85.7%, and 93.3%, respectively; and specificity: 94.1%, 91.7%, and 88.9%, respectively. When all the age groups were assessed together, the following values were obtained: accuracy: 87.5%, sensitivity: 83.3%, and specificity: 93.3%. CONCLUSIONS: It was observed that the CAD system developed in the study performed better in diagnosing pancreatic cancer images based on classification by patient age compared to diagnosis without classification. Therefore, it is imperative to take patient age into consideration to ensure higher performance.