Comparative Analysis of Deep Learning Architectures for Breast Cancer Diagnosis Using the BreaKHis Dataset

Cancer is an extremely difficult and dangerous health problem because it manifests in so many different ways and affects so many different organs and tissues. The primary goal of this research was to evaluate deep learning models' ability to correctly identify breast cancer cases using the BreakHis dataset. The BreakHis dataset covers a wide range of breast cancer subtypes through its huge collection of histopathological pictures. In this study, we use and compare the performance of five well-known deep learning models for cancer classification: VGG, ResNet, Xception, Inception, and InceptionResNet. The results placed the Xception model at the top, with an F1 score of 0.9 and an accuracy of 89%. At the same time, the Inception and InceptionResNet models both hit accuracy of 87% . However, the F1 score for the Inception model was 87, while that for the InceptionResNet model was 86. These results demonstrate the importance of deep learning methods in making correct breast cancer diagnoses. This highlights the potential to provide improved diagnostic services to patients. The findings of this study not only improve current methods of cancer diagnosis, but also make significant contributions to the creation of new and improved cancer treatment strategies. In a nutshell, the results of this study represent a major advancement in the direction of achieving these vital healthcare goals.Comment: 7 pages, 1 figure, 2 table

Dental technicians' pneumoconiosis; illness behind a healthy smile – case series of a reference center in Turkey

Background: Dental laboratories include many hazards and risks. Dental technicians working in an unfavorable work environment in Turkey and other parts of the world may develop pneumoconiosis as a result of exposure to dust, depending on exposure time. In this study, we aimed to investigate the clinical and laboratory findings of dental technicians. Materials and Methods: The study consists of a case series. Between 2013 and 2016, a total of 70 who were working as a dental technician and referred to our clinic with suspicion of occupational disease were evaluated. Comprehensive work-history, physical examination complaints, functional status, chest X-ray, and high-resolution computed lung tomography (HRCT) findings were evaluated. Results: In all, 46 (65.7%) of the 70 dental technicians were diagnosed with pneumoconiosis. About 45 (97.8%) subjects were male and 1 (2.2%) was female. The mean age of starting to work was 15.89 +/- 2.79 (11-23) years. The mix dust exposure time was 176.13 +/- 73.97 (18-384) months. Small round opacities were most common finding. In 16 patients, high profusion being 2/3 and above were identified, and large opacity was detected in 11 patients. The radiological profusion had a weak negative correlation with FEV 1 and FVC (correlation coefficient -0.18, P = 0.210 and -0.058, P = 0704) and moderate negative correlation between radiological profusion and FEV1/FVC (correlation coefficient -0.377, P = 0.010). In addition, no correlation was observed between the age at start of work and the duration of exposure. Conclusion: The presence of pneumoconiosis continues in dental technicians in Turkey, especially because there is an early childhood apprenticeship culture and almost all workers in this period have the history of sandblasting

The prevalence and topographic distribution of penile calcification in a large cohort: a retrospective cross-sectional study

The prevalence of penile calcification in the population remains uncertain. This retrospective multicenter study aimed to determine the prevalence and characteristics of penile calcification in a large cohort of male patients undergoing non-contrast pelvic tomography. A total of 14 545 scans obtained from 19 participating centers between 2016 and 2022 were retrospectively analyzed within a 3-months period. Eligible scans (n = 12 709) were included in the analysis. Patient age, penile imaging status, presence of calcified plaque, and plaque measurements were recorded. Statistical analysis was performed to assess the relationships between calcified plaque, patient age, plaque characteristics, and plaque location. Among the analyzed scans, 767 (6.04%) patients were found to have at least one calcified plaque. Patients with calcified plaque had a significantly higher median age (64 years (IQR 56–72)) compared to those with normal penile evaluation (49 years (IQR 36-60) (p < 0.001). Of the patients with calcified plaque, 46.4% had only one plaque, while 53.6% had multiple plaques. There was a positive correlation between age and the number of plaques (r = 0.31, p < 0.001). The average dimensions of the calcified plaques were as follows: width: 3.9 ± 5 mm, length: 5.3 ± 5.2 mm, height: 3.5 ± 3.2 mm, with an average plaque area of 29 ± 165 mm² and mean plaque volume of 269 ± 3187 mm³. Plaques were predominantly located in the proximal and mid-penile regions (44.1% and 40.5%, respectively), with 77.7% located on the dorsal side of the penis. The hardness level of plaques, assessed by Hounsfield units, median of 362 (IQR 250–487) (range: 100–1400). Patients with multiple plaques had significantly higher Hounsfield unit values compared to those with a single plaque (p = 0.003). Our study revealed that patients with calcified plaques are older and have multiple plaques predominantly located on the dorsal and proximal side of the penis