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

Anatomical Landmarks of the Beard Region

BACKGROUND With recent advancements in hair restoration techniques, hair can be transplanted into nonscalp areas, such as the beard region, and the result looks natural. Although scalp zones and landmarks have been available for planning and designing the hairline, landmarks that will determine the beard lines are yet to be presented and made known for clinical practice. OBJECTIVE This study aimed to determine and analyze the beard lines and anatomical boundaries of the beard area to provide a more natural restoration/reconstruction appearance. MATERIALS AND METHODS The soft-tissue landmarks of the face that will enable physicians to create natural-looking beard lines were determined. Based on these important points, beard lines were analyzed with anthropometric methods by using the photographs of 32 male volunteers. RESULTS The ideal upper and lower beard lines and the anatomical boundaries of the beard area were determined using these landmarks. CONCLUSION These lines ensure that beard restoration/reconstruction provides a more natural appearance

Mandibular actinomycosis mimicking tumor recurrence

Actinomycosis is a rare disease caused by a microorganism of the normal oral flora. Infection is characterized by swollen tissues and sinuses from which pus drains containing characteristic sulfur granules. Since actinomycosis is a tumorous infection of skin and subcutaneous tissue, this chronic lesion can occasionally mimic neoplasia. A male patient is presented; he had a tumor-like lesion of the left mental region that was initially diagnosed as tumor recurrence, but proved to be an Actinomyces infection on histopathologic examination. Excellent therapeutic response was obtained with a combination of antibiotic therapy, surgical debridement, and mandibular curettage. Vestibuloplasty-commissuroplasty was also performed