Analysis of framelets for breast cancer diagnosis

An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine

Skin is the outermost and largest organ of the human body that protects us from the external agents. Among the various types of diseases affecting the skin, melanoma (skin cancer) is the most dangerous and deadliest disease. Though it is one of the dangerous forms of cancer, it has a high survival rate if and only if it is diagnosed at the earliest. In this study, skin cancer classification (SCC) system is developed using dermoscopic images. It is considered as a classification problem with the help of Bendlet Transform (BT) as features and Support Vector Machine (SVM) as a classifier. First, the unwanted information's such as hair and noises are removed using median filtering approach. Then, directional representation based feature extraction system that precisely classifies curvature, location and orientation is employed. Finally, two SVM classifiers are designed for the classification. The performance of the SCC system based on Bendlet is superior to other image representation systems such as Wavelets, Curvelets, Contourlets and Shearlets.

Combined diagnosis of breast cancer in the early stage by MRI and detection of gene expression

Breast cancer is one of the most common cancer types in humans. Magnetic resonance imaging (MRI) is an efficient method for the detection of human breast cancer. However, the efficacy of MRI in detecting breast cancer in the early stage requires to be improved. The present study investigated the diagnostic efficacy of a combination of MRI and detection of gene expression in patients with breast cancer in the early stage. The gene expression levels of Ki-67, BCL11A, FOXC1, HOXD13, PCDHGB7 and her-2 were used as an auxiliary diagnostic index for patients with breast cancer in the early stage. Higher expression levels of TPA and C2erbB22 were observed in tumor tissue obtained from diagnostic biopsy and determined by immunohistochemistry, which indicated a higher risk of breast cancer in a total of 84 participants. Diagnostic data revealed that combination MRI and detection of gene expression had a significantly higher diagnostic rate (66/84) in diagnosing breast cancer in an early stage compared with either MRI (78/360) or detection of gene expression (72/84; P<0.01). It was indicated that the combination of MRI and detection of gene expression had a higher diagnostic rate (94.5%) than either MRI (81.4%) or detection of gene expression (75.5%). Histological analysis confirmed the diagnosis determined by MRI and detection of gene expression. These results suggest that the combination of MRI and detection of gene expression may be a potential diagnostic method for assessing patients with early-stage breast cancer.