Microscopic handcrafted features selection from computed tomography scans for early stage lungs cancer diagnosis using hybrid classifiers.

Lung's cancer is the leading cause of cancer-related deaths worldwide. Recently cancer mortality rate and incidence increased exponentially. Many patients with lung cancer are diagnosed late, so the survival rate is shallow. Machine learning approaches have been widely used to increase the effectiveness of cancer detection at an early stage. Even while these methods are efficient in detecting specific forms of cancer, there is no known technique that could be used universally and consistently to identify new malignancies. As a result, cancer diagnosis via machine learning algorithms is still fresh area of research. Computed tomography (CT) images are frequently employed for early cancer detection and diagnosis because they contain significant information. In this research, an automated lung cancer detection and classification framework is proposed which consists of preprocessing, three patches local binary pattern feature encoding, local binary pattern, histogram of oriented gradients features are extracted and fused. The fast learning network (FLN) is a novel machine-learning technique that is fast to train and economical in terms of processing resources. However, the FLN's internal power parameters (weight and basis) are randomly initialized, resulting it an unstable algorithm. Therefore, to enhance accuracy, FLN is hybrid with K-nearest neighbors to classify texture and appearance-based features of lung chest CT scans from Kaggle dataset into cancerous and non-cancerous images. The proposed model performance is evaluated using accuracy, sensitivity, specificity on the Kaggle benchmark dataset that is found comparable in state of the art using simple machine learning strategies. RESEARCH HIGHLIGHTS: Fast learning network and K-nearest neighbor hybrid classifier proposed first time for lung cancer classification using handcrafted features including three patches local binary pattern, local binary pattern, and histogram of oriented gradients. Promising results obtained from novel simple combination.

A Comparative Evaluation on the Effect of Oral Zinc 50 mg with or without 0.1% Triamcinolone Orabase on Oral Lichen Planus.

BACKGROUND: Lichen planus is a T-cell-mediated autoimmune disease, in which CD8+ T-cells releases the cytokines such as tumor necrosis factor-alpha and interleukin-12 disrupting basement membrane integrity. Treatment modalities were directed toward the relief in signs and symptoms and preventing recurrences. Zinc activates caspase-3 and DNA fragmentation, resulting in the apoptosis of keratinocytes. Prevention of matrix metalloproteinases1 (MMP1) activation, inhibits the Tcell accumulation in oral lichen planus (OLP) and by inhibiting MMP9, prevents the cleavage of collagen resulting in maintaining the integrity of the basement membrane. OBJECTIVES: The main objective of the study is to compare the efficacy of oral zinc 50 mg and 0.1% triamcinolone Orabase with 0.1% triamcinolone Orabase alone on the healing process of symptomatic OLP. MATERIALS AND METHODS: A total of forty participants were randomly categorized into two groups: Group A and Group B with 20 patients with OLP and having symptoms of burning sensation. Group A patients had received 0.1% triamcinolone Orabase twice daily application. Group B patients had provided with oral zinc 50 mg and 0.1% triamcinolone Orabase twice daily for 8 weeks. The follow-up period for both the groups was 6 months. Lesional size was measured by Thongprasom scale and burning sensation was assessed by visual analog scale at each visit till the cessation of treatment. RESULTS: There was decrease in the burning sensation and lesional size from the first visit to follow-up period which was statistically significant in both groups (P = 0.000). CONCLUSION: Oral zinc therapy was adjunctive in reducing the burning sensation and lesional size in the symptomatic OLP.