HEP-2 CELL IMAGES FLUORESCENCE INTENSITY CLASSIFICATION TO DETERMINE POSITIVITY BASED ON NEURAL NETWORK AMIN

Nowadays, the recommended method for detection of anti-nuclear auto-antibodies is by using Indirect Immunofluorescence (IIF). The increasing of test demands on classification of Hep-2 cell images force the physicians to carry out the test faster, resulting bad quality results. IIF diagnosis requires estimating the fluorescence intensity of the serum and this will be observed. As there are subjective and inter/intra laboratory perception of the results, the development of computer-aided diagnosis (CAD) tools is used to support the decision. In this report, we propose the classification technique based on Artificial Neural Network (ANN) that can classify the Hep-2 cell images into 3 classes namely positive, negative and intermediate,specifically to determine the presence of antinuclear autoantibodies (ANA)

Multiplex Technology for Biomarker Immunoassays

The simultaneous measurement of different substances from a single sample is an emerging area for achieving efficient and high-throughput detection in several applications. Although immunoanalytical techniques are established and advantageous over alternative screening analytical platforms, one of the challenges for immunoassays is multiplexing. While ELISA is still commonly used to characterise a single analyte, laboratories and organisations are moving towards multiplex immunoassays. The validation of novel biomarkers and their amalgamation into multiplex immunoassays confers the prospects of simultaneous measurement of multiple analytes in a single sample, thereby minimising cost, time and sample. Therefore, the technological advancement in clinical sciences is helpful in the identification of analytes or biomarkers in test samples. However, the analytical bioanalysers are expensive and capable of detecting only a small amount or type of analytes. The simultaneous measurement of different substances from a single sample called multiplexing has become increasingly important for the quantification of pathological or toxicological samples. Although multiplex assays have many advantages over conventional assays, there are also problems that may cause apprehension among clinicians and researchers. Hence, many challenges still remain for these multiplexing systems which are at early stages of development

Immunoassay Techniques Highlighting Biomarkers in Immunogenetic Diseases

Diagnosis of autoimmune diseases is crucial for the clinician and the patient alike. The immunoassay techniques most commonly used for this purpose are immunohistochemistry, ELISA, and Western blotting. For the detection of more specific biomarkers or the discovery of new ones for diagnostic purposes and as therapeutic targets, microarray techniques are increasingly used, for example, protein microarray, Luminex, and in recent years, surface plasmon resonance imaging. All of these technologies have undergone changes over time, making them easier to use. Similar technologies have been invented but responding to specific requirements for both diagnostic and research purposes. The goals are to study more analytes in the same sample, in a shorter time, and with increased accuracy. The reproducibility and reliability of the results are also a target pursued by manufacturers. In this chapter, we present these technologies and their utility in the diagnosis of immunogenetic diseases

HEP-2 CELL IMAGES FLUORESCENCE INTENSITY CLASSIFICATION TO DETERMINE POSITIVITY BASED ON NEURAL NETWORK AMIN

Nowadays, the recommended method for detection of anti-nuclear auto-antibodies is by using Indirect Immunofluorescence (IIF). The increasing of test demands on classification of Hep-2 cell images force the physicians to carry out the test faster, resulting bad quality results. IIF diagnosis requires estimating the fluorescence intensity of the serum and this will be observed. As there are subjective and inter/intra laboratory perception of the results, the development of computer-aided diagnosis (CAD) tools is used to support the decision. In this report, we propose the classification technique based on Artificial Neural Network (ANN) that can classify the Hep-2 cell images into 3 classes namely positive, negative and intermediate,specifically to determine the presence of antinuclear autoantibodies (ANA)

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The classification at the image-level was obtained by analyzing the pattern prevalence at cell-level. The layers of the pre-trained network and various system parameters were evaluated in order to optimize the process. This system has been developed and tested on the HEp-2 images indirect immunofluorescence images analysis (I3A) public database. To test the generalisation performance of the method, the leave-one-specimen-out procedure was used in this work. The performance analysis showed an accuracy of 96.4% and a mean class accuracy equal to 93.8%. The results have been evaluated comparing them with some of the most representative works using the same database

FLUORESCENCE INTENSITY POSITIVITY CLASSIFICATION OF HEP-2 CELLS IMAGES USING FUZZY LOGIC

Indirect Immunofluorescence (IIF) is a gold standard used for antinuclear autoantibody (ANA) test using Hep-2 cells to determine specific diseases. Automated interpretation is crucial to assure high accuracy to determine the autoantibody type of diseases. There are different classifier algorithm methods that have been proposed in previous works to classify the fluorescence intensity, however, there is still no valid algorithms to set as a standard. The purpose of this study is to classify the fluorescence intensity by using fuzzy logic algorithm to determine the positivity of the Hep2-cell serum samples. The scope of study of this project involves converting the RGB colour space of images to LAB colour space and the mean value of the lightness channel and chromaticity layer (a) channel is extracted and classified by using fuzzy logic algorithm based on the standard score ranges of ANA fluorescence intensity which are 4+, 3+, 2+, 1+ and 0. Based on the results, the accuracy of intermediate and positive class is 85% and 87% respectively

An Intelligent Detection System for Rheumatoid Arthritis (RA) Disease using Image Processing

Rheumatoid Arthritis (RA) is an autoimmune disease that causes chronic pain, stiffness, redness or loss of function in the joints. Other than early diagnosis, there is yet a cure available for RA. Diseases with similar symptoms such as lupus, osteoarthritis, gout cause difficulty in diagnosing RA. Currently, indirect immunofluorescence (IIF) test performed to identify ANA in Hep-2 cells. Thus, image processing techniques vital to make diagnosis more efficient, accurate and less time-consuming. For this project standardized staining pattern classifier to be designed by using image processing techniques. Current manual techniques has limited accuracy and time consuming. In IFF procedures, unsuitable microscope to read Hep-2 cell slides, or photo bleaching effect where cells bleached extremely in short period of time are disadvantages. Another downside is test results being subject to change with experts knowledge and years of experience. These factors lead to low accuracy and it becomes a lengthy process due to large number of images. Out of five types of staining patterns nucleolar and centromere share similar visual appearance and the same is true to homogeneous, fine-speckled, coarse-speckled patterns. This is one of the major factors affecting classification accuracy due to results being subjective. In this research, First and Second Order Statistics Feature Extraction, Mamdani Fuzzy Logic Classification methods utilized to develop automatic detection system for RA with the help of Matlab R2012b, Fuzzy Logic Toolbox, and Image Processing Toolbox. The algorithm tested on the publicly available Mivia Hep-2 Cell image dataset. Fuzzy logic classified 85 out of 250 images wrongly. It has 66% accuracy. The images obtained from MIVIA dataset has been manually segmented to cell level from the image level. Developing an automated segmentation algorithm might give better results

Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells' existence