Recognition and classification of traffic signs and other numerous displays on the road are very crucial for autonomous driving, navigation, and safety systems on roads. Machine learning or deep learning methods are generally employed to develop a traffic sign recognition (TSR) system. This paper proposes a novel two-step TSR approach consisting of contrast limited adaptive histogram equalization (CLAHE)-based image enhancement and convolutional neural network (CNN) as multiclass classifier. Three CNN architectures viz. LeNet, VggNet, and ResNet were employed for classification. All the methods were tested for classification of German traffic sign recognition benchmark (GTSRB) dataset. The experimental results presented in the paper endorse the capability of the proposed work. Based on experimental results, it has also been illustrated that the proposed novel architecture consisting of CLAHE-based image enhancement & ResNet-based classifier has helped to obtain better classification accuracy as compared to other similar approaches.

No abstract

No abstract

HE4 is a secretory protein. It is expressed in reproductive tract and respiratory epithelium in normal individuals. Serum level of HE4 is raised in various solid cancers that give us an advantage to use it as a diagnostic and prognostic biomarker. It is an established biomarker of epithelial ovarian cancer [EOC]. It has also shown the significance in various other malignancies like cancer of endometrium, cervix, lung and breast. Studies show HE4 as an independent prognostic biomarker in non-small cell lung carcinoma. Its raised values in cancer signify its role in oncogenesis. HE4 promotes angiogenesis via STAT3 signalling pathway. In this paper we have tried to illustrate about human epididymis protein 4 and its role in tumour angiogenesis.

No abstract