A novel approach to jointly address localization and classification of breast cancer using bio-inspired approach

Localization of the cancerous region as well as classification of the type of the cancer is highly inter-linked with each other. However, investigation towards existing approaches depicts that these problems are always iindividually solved where there is still a big research gap for a generalized solution towards addressing both the problems. Therefore, the proposed manuscript presents a simple, novel, and less-iterative computational model that jointly address the localization-classification problems taking the case study of early diagnosis of breast cancer. The proposed study harnesses the potential of simple bio-inspired optimization technique in order to obtained better local and global best outcome to confirm the accuracy of the outcome. The study outcome of the proposed system exhibits that proposed system offers higher accuracy and lower response time in contrast with other existing classifiers that are freqently witnessed in existing approaches of classification in medical image process

Automated Analysis of Chest Radiographs for Cystic Fibrosis Scoring

We present a framework to analyze chest radiographs for cystic fibro-sis using machine learning methods. We compare the representational power of deep learning features with traditional texture features. Specifically, we respec-tively employ VGG-16 based deep learning features, Tamura and Gabor filter based textural features to represent the cystic fibrosis images. We demonstrate that VGG-16 features perform best, with a maximum agreement of 82%. In ad-dition, due to limited dimensionality, Tamura features for unsegmented images achieve no more than 50% agreement; however, after segmentation, the accuracy of Tamura can reach 78%. In combination with using the deep learning features, we also compare back propagation neural network and sparse coding classifiers to the typical SVM classifier with polynomial kernel function. The result shows that neural network and sparse coding classifiers outperform SVM in most cases. Only with insufficient training samples does SVM demonstrate higher accuracy

Robust Content Identification and De-Duplication with Scalable Fisher Vector In video with Temporal Sampling

Title from PDF of title page, viewed august 29, 2017Thesis advisor: Zhu LiVitaIncludes bibliographical references (pages 41-43)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017Robust content identification and de-duplication of video content in networks and caches have many important applications in content delivery networks. In this work, we propose a scalable hashing scheme based Fisher Vector aggregation of selected key point features, and a frame significance function based non-uniform temporal sampling scheme on the video segments, to create a very compact binary representation of the content fragments that is agnostic to the typical coding and transcoding variations. The key innovations are a key point repeatability model that selects the best key point features, and a non-uniform sampling scheme that significantly reduces the bits required to represent a segment, and scalability from PCA feature dimension reduction and Fisher Vector features, and Simulation with various frame size and bit rate video contents for DASH streaming are tested and the proposed solution have very good performance of precision-recall, achieving 100% precision in duplication detection with recalls at 98% and above range.Introduction -- Software description -- Image processing -- SIFT feature extraction -- Principal component analysis -- Fisher vector aggregation -- Simulation results and discussions -- Conclusion and future work -- Appendi

Novel Deep Learning Models for Medical Imaging Analysis

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201