Classification of malignant and benign lung nodule and prediction of image label class using multi-deep model

Lung cancer has been listed as one of the world’s leading causes of death. Early diagnosis of lung nodules has great significance for the prevention of lung cancer. Despite major improvements in modern diagnosis and treatment, the five-year survival rate is only 18%. Before diagnosis, the classification of lung nodules is one important step, in particular, because automatic classification may help doctors with a valuable opinion. Although deep learning has shown improvement in the image classifications over traditional approaches, which focus on handcraft features, due to a large number of intra-class variational images and the inter-class similar images due to various imaging modalities, it remains challenging to classify lung nodule. In this paper, a multi-deep model (MD model) is proposed for lung nodule classification as well as to predict the image label class. This model is based on three phases that include multi-scale dilated convolutional blocks (MsDc), dual deep convolutional neural networks (DCNN A/B), and multi-task learning component (MTLc). Initially, the multi-scale features are derived through the MsDc process by using different dilated rates to enlarge the respective area. This technique is applied to a pair of images. Such images are accepted by dual DCNNs, and both models can learn mutually from each other in order to enhance the model accuracy. To further improve the performance of the proposed model, the output from both DCNNs split into two portions. The multi-task learning part is used to evaluate whether the input image pair is in the same group or not and also helps to classify them between benign and malignant. Furthermore, it can provide positive guidance if there is an error. Both the intra-class and inter-class (variation and similarity) of a dataset itself increase the efficiency of single DCNN. The effectiveness of mentioned technique is tested empirically by using the popular Lung Image Consortium Database (LIDC) dataset. The results show that the strategy is highly efficient in the form of sensitivity of 90.67%, specificity 90.80%, and accuracy of 90.73%

Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis'). The manuscript is available from following link (https://doi.org/10.1016/j.media.2019.06.005

Subfigure and Multi-Label Classification using a Fine-Tuned Convolutional Neural Network

Abstract. This paper describes the submission of the BMET group to the Subfigure Classification and Multi-Label Classification tasks of the ImageCLEF 2016 medical subtrack. Our method creates a new optimised feature extractor by using medical images to fine-tune a CNN that has been pre-trained on general image data. Our classification method shows promising result in both the the subfigure classification and multi-label classification subtasks