Objectives Now a days, squamous cell carcinoma (SCC) margin assessment is done by examining histopathology images and inspection of whole slide images (WSI) using a conventional microscope. This is time-consuming, tedious, and depends on experts’ experience which may lead to misdiagnosis and mistreatment plans. This study aims to develop a system for the automatic diagnosis of skin cancer margin for squamous cell carcinoma from histopathology microscopic images by applying deep learning techniques. Methods The system was trained, validated, and tested using histopathology images of SCC cancer locally acquired from Jimma Medical Center Pathology Department from seven different skin sites using an Olympus digital microscope. All images were preprocessed and trained with transfer learning pre-trained models by fine-tuning the hyper-parameter of the selected models. Results The overall best training accuracy of the models become 95.3%, 97.1%, 89.8%, and 89.9% on EffecientNetB0, MobileNetv2, ResNet50, VGG16 respectively. In addition to this, the best validation accuracy of the models was 94.7%, 91.8%, 87.8%, and 86.7% respectively. The best testing accuracy of the models at the same epoch was 95.2%, 91.5%, 87%, and 85.5% respectively. From these models, EfficientNetB0 showed the best average training and testing accuracy than the other models. Conclusions The system assists the pathologist during the margin assessment of SCC by decreasing the diagnosis time from an average of 25 minutes to less than a minute.
Background: Tumors of salivary gland are morphologically and clinically diverse group of neoplasms, which may present considerable diagnostic and management of challenges to the pathologist or surgeons.It account’s about 4% of all epithelial neoplasms in head and neck region. These comprise a wide variety of benign and malignant neoplasms, and non-neoplastic lesions which exhibit a difference in biological behaviors. Therefor it is important to study the morphological patterns of salivary gland neoplastic and non-neoplastic lesions.
Methods and Materials: A retrospective cross-sectional study design was applied for Salivary gland lesions patients diagnosed at Jimma university medical center from September 2016 to August 2020.
Results: from the total of 176 patients 135(76.7%) were neoplastic and the remaining 41(23.3%) were non-neoplastic lesions. Being in the age group of 21-40[odds ratio=5.172, 41-60[odds ratio =4.534], and having the lesions for duration >24 months [odds ratio 12.479] and the size of the mass >5 cm [odds ratio =19.486]were associated increased odds of neoplastic lesions, while the site of the lesions being in major groups of salivary glands [odds ratio=0.056] was associated with decreased likelihood of neoplastic Salivary gland lesions.
Conclusion: The prevalence of neoplastic salivary lesions was three times more common than non-neoplastic ones and malignancies were slightly more common than benign lesions. Mucoepidermoid carcinoma was the commonest malignant while Pleomorphic adenoma was the commonest benign Salivary gland lesions both in females and males. The neoplastic Salivary gland lesions were highly associated with age, duration, tumor size and minor groups of salivary glands.
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