Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer- Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists

Achieving Accuracy in Early Stage Tumor Identification Systems based on Image Segmentation and 3D Structure Analysis

Cancer is a disease which can be removed if early stage tumor identification systems efficiently and accurately work at cancer hospitals. As the accuracy in detection of tumor means to detect exact size of the tumor. Because the best way to beat cancer is early stage tumor diagnosis and quality treatment. In this research article an accuracy module is proposed for computer aided tumor diagnosis system. The ultimate proposed CAD gets image of tumor infected lung and breast images from different state of the art early stage tumor detection methodologies as micrographic and mammographic based imaging systems. For accuracy in detection of early stage tumor, image enhancement and segmentation techniques are applied according to the imaging problems at input image. Also for accurate estimation of tumor the 3D image construction and 3D structure analysis are tried to realized. The realization of the proposed CAD proves that the accuracy module can assist well the computer aided tumor diagnosis systems with almost near to 100% accuracy in early stage tumor detection and size estimation for breast and lung cancer. Keywords: Computer Aided Tumor Detection, Accurate identificatio

AI-enhanced diagnosis of challenging lesions in breast MRI: a methodology and application primer

Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist’s review. CAD systems have many common parts such as image pre-processing, tumor feature extraction and data classification that are mostly based on machine learning (ML) techniques. In this review paper, we describe the application of ML-based CAD systems in MRI of the breast covering the detection of diagnostically challenging lesions such as non-mass enhancing (NME) lesions, multiparametric MRI, neo-adjuvant chemotherapy (NAC) and radiomics all applied to NME. Since ML has been widely used in the medical imaging community, we provide an overview about the state-ofthe-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples illustrating: (i) CAD for the detection and diagnosis, (ii) CAD in multi-parametric imaging (iii) CAD in NAC and (iv) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on ANN in MRI of the breast

Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis

Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Microcalcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system.We have developed a novel method for the classification of benign and malignant microcalcification using an improved Fisher Linear Discriminant Analysis (LDA) approach for the linear transformation of segmented micro-calcification data in combination with a Support Vector Machine (SVM) variant to classify between the two classes. The results indicate an average accuracy equal to 96% which is comparable to state-of-the art methods in the literature.authorsversionPeer reviewe

Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks

Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial for making treatment decisions, but can be challenging even for experienced radiologists. The diagnostic procedure is based on the detection and recognition of the different ILD pathologies in thoracic CT scans, yet their manifestation often appears similar. In this study, we propose the use of a deep purely convolutional neural network for the semantic segmentation of ILD patterns, as the basic component of a computer aided diagnosis (CAD) system for ILDs. The proposed CNN, which consists of convolutional layers with dilated filters, takes as input a lung CT image of arbitrary size and outputs the corresponding label map. We trained and tested the network on a dataset of 172 sparsely annotated CT scans, within a cross-validation scheme. The training was performed in an end-to-end and semi-supervised fashion, utilizing both labeled and non-labeled image regions. The experimental results show significant performance improvement with respect to the state of the art

A Self-adaptive Discriminative Autoencoder for Medical Applications

Computer aided diagnosis (CAD) systems play an essential role in the early detection and diagnosis of developing disease for medical applications. In order to obtain the highly recognizable representation for the medical images, a self-adaptive discriminative autoencoder (SADAE) is proposed in this paper. The proposed SADAE system is implemented under a deep metric learning framework which consists of K local autoencoders, employed to learn the K subspaces that represent the diverse distribution of the underlying data, and a global autoencoder to restrict the spatial scale of the learned representation of images. Such community of autoencoders is aided by a self-adaptive metric learning method that extracts the discriminative features to recognize the different categories in the given images. The quality of the extracted features by SADAE is compared against that of those extracted by other state-of-the-art deep learning and metric learning methods on five popular medical image data sets. The experimental results demonstrate that the medical image recognition results gained by SADAE are much improved over those by the alternatives

Automated Brain Abnormality Detection through MR Images

Brain diseases one of the major cause of cancer-related death among children and adults in the world. Brain diseases like brain tumor is characterized as a gathering of abnormal cells that becomes inside the brain and around the brain.There are various imaging techniques which are used for brain tumor detection. Among all imaging technique, MRI (Magnetic Resonance Imaging) is widely used for the brain tumor detection. MRI is safe, fast and non-invasive imaging technique. The early detection of brain diseases is very important, for that CAD (Computer-aided-diagnosis) systems are used. The proposed scheme develops a new CAD system in which pulse-coupled neural network is used for the brain tumor segmentation from MRI images. After segmentation, for feature extraction the Discrete Wavelet Transform and Curvelet Transform are employed separately. Subsequently, both PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) have been applied individually for feature reduction. A standard dataset of 101 brain MRI images (14 normal and 87 abnormal) is utilized to validate the proposed scheme. The experimental results show that the suggested scheme achieves better result than the state-of-the-art techniques with a very less number of features

Image Features for Tuberculosis Classification in Digital Chest Radiographs

Tuberculosis (TB) is a respiratory disease which affects millions of people each year, accounting for the tenth leading cause of death worldwide, and is especially prevalent in underdeveloped regions where access to adequate medical care may be limited. Analysis of digital chest radiographs (CXRs) is a common and inexpensive method for the diagnosis of TB; however, a trained radiologist is required to interpret the results, and is subject to human error. Computer-Aided Detection (CAD) systems are a promising machine-learning based solution to automate the diagnosis of TB from CXR images. As the dimensionality of a high-resolution CXR image is very large, image features are used to describe the CXR image in a lower dimension while preserving the elements in the CXR necessary for the detection of TB. In this thesis, I present a set of image features using Pyramid Histogram of Oriented Gradients, Local Binary Patterns, and Principal Component Analysis which provides high classifier performance on two publicly available CXR datasets, and compare my results to current state-of-the-art research