Object detection using hybridization of static and dynamic feature spaces and its exploitation by ensemble classification

VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.

A Classification Method of Oracle Materials Based on Local Convolutional Neural Network Framework

The classification of materials of oracle bone is one of the most basic aspects for oracle bone morphology. However, the classification method depending on experts' experience requires long-term learning and accumulation for professional knowledge. This article presents a multiregional convolutional neural network to classify the rubbings of oracle bones. First, we detected the "shield grain" and "tooth grain" on the oracle bone rubbings, then complete the division of multiple areas on an image of oracle bone. Second, the convolutional neural network is used to extract the features of each region and we complete the fusion of multiple local features. Finally, the classification of tortoise shell and animal bone was realized. Utilizing the image of oracle bone provided by experts, we conducted an experiment; the result show our method has better classification accuracy. It has made contributions to the progress of the study of oracle bone morphology.