COMSATS University Islamabad __Lahore__ Campus Synopsis for the degree of M

COMSATS University Islamabad __Lahore__ Campus Synopsis for the degree of M.S./M.Phil Ph.D PART-1 (to be completed by the student) Name of StudentRubab AbrarDepartmentElectrical EngineeringRegistration No. CIIT/SP17-REE-001/LHRName of (i) Research Supervisor Dr. Ikramullah KhosaMembers of Supervisory Committee1.Dr. 2.Dr. 3.Dr. Major Field of Study Master of Science in Electrical EngineeringField of SpecializationComputer System EngineeringTitle of Research Proposal Summary of Research Proposal Breast cancer is very common virus among ladies all over the world it is the second primary cause of death for ladies. Pathology examination needs time looking over tissue images under various magnification levels to discover clinical assessment clues to deliver correct analyse. Advances in digital imaging systems offers evaluation of pathology images utilizing computer vision and machine learning techniques which could systematize some of the tasks in the analytical pathology work process. In this work, we propose to characterize breast cancer histopathology images autonomous of their various magnifications using convolutional neural networks (CNNs). We propose two various designs single task CNN is to predict malignancy and multi-task CNN is to predict malignancy and image magnification levels instantaneously. Evaluations and comparisons with previous results are carried out on BreaKHis dataset. Experimental results show that our magnification autonomous CNN approach improved the performance of magnification specific model. Introduction Cancer is one of the leading cause of death. And breast cancer is very common cancer among ladies. A biopsy taken after by microscopic investigation of breast tissue is essential for a definitive diagnosis of breast cancer Firstly, little sections are annul from biopsy apparatus and by the time mentioned stained consistently by all of hematoxylin and eosin (HE). Haematoxylin highpoints nuclei by binding DNA and eosin highpoints distinctive structures by all locked up proteins. Visualization is the a while later step at the heels of staining. Finally, pathologists use tissue biopsies under microscope under distinct magnifications or on the digital image with no appreciable competition in diagnoses. Pathologist examines tissue patterns, textures, and morphology to see clinical reckoning clues to produce approved diagnoses. They may need to focus, zoom, and scan over the perfect image at great magnification which may be a matter of fact that consume a lot of time. The advances in digital imaging procedures enable digitizing pathology images at insignificant resolution. This habit offers the histological appraisal of haematoxylin and eosin stained sections by computer flight of imagination and gear learning methods. These methods could auto mate some of the tasks in the detailed pathology workflow which used to reduce observer variability and take turn for better objectivity. In opening, fast and actual quantification could gain the healthcare quality. However, this requires fit examination algorithms which are still easily from impartial acceptance. Nonetheless, there has been a made up for lost time in the society of image examination algorithms for histopathological image assessment. For in a superior way information on modern image cut and try methods in histology. Appearance variability of hematoxylin and eosin stained sections is such of the masterpiece challenges in breast cancer histopathology image (see Figure 1). These variations because of variability among group, differences in protocols during labs, repression, specimen attitude in the obstruct, human skills in tissue training, microscopy load off one mind, and boast variation discipline to differences in staining procedures. However, the main confront for image researchers is accessing images and databases. Development of more rational data examination methods expected applied in pathological diagnosis could be facilitated by willingly available datasets which is currently lacking. Such datasets and benchmarks score validating and comparing algorithms to shake more ones. Recently, a dataset of breast cancer histopathology images (BreaKHis) is reported for this purpose. It was stored from 82 patients using various magnifying factors (40x 100x 200x 400x) (Figure 2). The database covers 2,480 benign and 5,429 malignant images. Moreover, the training delivers the classification performance of numerous hand-crafted textural structures as baselines to distinguish among benign and malignant tumors. In this paper, we suggest to categorize breast cancer histopathology images autonomous of their magnifications. We basically present the classification presentation of a deep learning technique on the BreaKHis dataset in order to deliver extra model. Moreover, we suggest identifying the image magnification level and categorizing benign and malignant tumors instantaneously by using a similar system architecture which is modified for multi-task classification. Most prominently, we present a learning based method which could help from extra labelled training data in open manner, unlike the methods created on hand-crafted structures. Fig. 1. Sample breast cancer histopathology images from BreaKHis database. They were all acquired at a magnification factor of 100x Fig. 2. A malignant breast tumor acquired from a single slide seen in different magnification factors 40x 100x 200x, and 400x Literature Review During the investigation of the marked tissue, pathologists study overall tissue architecture, along with nuclei group, density and variability.Some works emphasis on nuclei study for malignant-benign classification. Kowalet al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref011 11 used various clustering algorithms for nuclei division on well needle biopsy microscopic images. Morphological, and texture structures were used for preparation a classifier, achieving an precision among 84 and 93 on 500 images as of 50 patients. Patient-wise arrangement was performed by common voting on 10 images each, with an precision of 96100. Likewise, Filipczuket al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref012 12 and Georgeet al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref013 13 take out nuclei-based structures from well needle biopsies. First, circular Hough transform was used for detecting nuclei candidates, after that false-positive reduction with machine-learning and Otsu thresholding. Georgeet al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref013 13 the nuclei segmentation is more refined by watershed. In both works, shape and texture structures of nuclei are used for training various classifiers. Filipczuket al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref012 12 succeeded an precision of 98.51, by popular voting over 11 images each of the 67 patients, and Georgeet al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref013 13 among 71.9 and 97.15 in distinct image classification using 92 images. Also nuclei-related information, Belsareet al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref014 14 considered tissue organization for the binary classification of more complex images. The authors valued 70 images from a private 40 magnification breastHEdataset. Other writers have focused on a complex 3-classes classification of breast cancer images. For example, both Brooket al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref015 15 and Zhanget al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref016 16 categorized breast cancer tissue images in normal,in situcarcinoma and aggressive carcinoma. Dataset from the Israel Institute was used HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref017 17. Brooket al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref015 15 finalized each image numerous threshold values and linked component to train a (SVM) classifier, reporting 93.4 regular precision that could be better to 96.4 by 20 of the images. Zhanget al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref016 16 used a cascade classification method. Curve let Transform and local binary pattern structures were arbitrarily fed to a parallel SVM classifiers. Where Images were given number of classifiers to disagree were rejected and analyzed by artificial neural networks (ANN) over other feature subsets. Again, images were rejected where the number of classifiers disagreed. This system achieved 97 precision with 0.8 rejection rate. The recent rise in computing power and dataset sizes allowable the application of Convolutional Neural Networks to image classification problems. Contrarily to the traditional method of hand-crafted structure extraction approaches, CNNs study useful features straight from the trained image patches by optimization of the classification function loss. These deep learning models have achieved outstanding performance in image classification tests in various fields HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref018 18, HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref019 19, including medical image analysis HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref020 20, and in precise on histopathology images HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref021 21. CNNs also allow to reduce the field-knowledge wanted to design a classification system. Because the performance of the approaches is less biased by dataset used and related network architectures can reach good results on various problems. Spanholet al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref022 22 used a CNN architecture moved in the Image network HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref018 18 to organizeHEbreast tissue biopsy trials in benign and malignant tumors, by multiple magnifications. In their work, 32 32 and 64 64 pixels patches were removed from the initial images and used for training the convolution neural network CNN. The final classification was attained by joining the patch probabilities with product, sum or maximum rules. Two patch abstraction methods were studied, sliding window and random abstraction. The abstraction of patches allowable reducing the difficulty of the model by decreasing the size of the input in layers. The writers reported an precision decrease for advanced magnifications, which advises that their CNN architecture cannot abstract relevant structures for advanced magnifications. In fact, for advanced magnifications only nuclei edge-related structures are extracted. Other writers have familiar the architecture of the CNN to breast histology related-problems with achievement. Ciresanet al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref019 19 used 101 101 patches to train a CNN for mitosis detection in HE stained breast slides. The used architecture allows studying nuclei of different sizes and their areas. This practice won the ICPR 2012 Mitosis Discovery Contest with a F1-score of 0.782. In Cruz-Roaet al. HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref023 23 a CNN was trained on 100 100 pixels whole-slide patches, removed using grid sampling, to notice invasive carcinoma regions in breast slides. Due to the global nature of the problem, their CNN feature-extraction measure ranges from nuclei to overall tissue institute. This technique outperformed other state-of-the-art approaches, achieving a F1-score of 0.780. For these works, the model was slided through the image to attain a probability map and then the recognition result was obtained via thresholding. In HYPERLINK http//journals.plos.org/plosone/articleid10.1371/journal.pone.0177544 l pone.0177544.ref019 19 the training dataset complexity were increased by relating arbitrary rotations and mirroring to the training instances. Statement of the Problem Research Methodology Block Diagram Bibliography Tentative Timeline Name of Student Signature and Date PART II Recommendation by the Research Supervisor Name Dr. Ikramullah Khosa ______ Signature _____________ Date ___________ Signed by Supervisory Committee S. Name of Committee Member Designation Signature and Date 1 Dr. Asim Ali Khan Assistant Professor 2 Dr. Muhammad Jawad Assistant Professor 3 Dr. Muhammad Haris Khan Assistant Professor Approved by Departmental Advisory Committee Certified that the synopsis has been seen by members of DAC and considered it suitable for putting up to BASAR. Secretary Departmental Advisory Committee Name Dr. Asim Ali Khan __ Signature__________________________ Date________________________ Chairman/HoD Dr. Saleem Akhtar Signature ______________________________ Date ________________ PART III Dean of Respective Faculty ________________ Approved for placement before BASAR. ________________ Not Approved on the basis of following reasons Signature ______________________________ Date ________________ Secretary BASAR ________________ Approved from BASAR. ________________ Not Approved on the basis of the following reasons Signature ______________________________ Date ________________ Page PAGE 5 of NUMPAGES 7 ( sWLAkz–F08BBkks(Pb7
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