Intelligent System for Detection of Abnormalities in Human Cancerous Cells and Tissues

Due to the latest advances in the field of MML (Medical Machine Learning) a significant change has been witnessed and traditional diagnostic procedures have been converted into DSS (Decision Support Systems). Specially, classification problem of cancer discovery using DICOM (Digital Communication in Medicine) would assume to be one of the most important problems. For example differentiation between the cancerous behaviours of chromatin deviations and nucleus related changes in a finite set of nuclei may support the cytologist during the cancer diagnostic process. In-order to assist the doctors during the cancer diagnosis, this paper proposes a novel algorithm BCC (Bag_of_cancerous_cells) to select the most significant histopathological features from the well-differentiated thyroid cancers. Methodology of proposed system comprises upon three layers. In first layer data preparation have been done by using BMF (Bag of Malignant Features) where each nuclei is separated with its related micro-architectural components and behaviours. In second layer decision model has been constructed by using CNN (Convolutional Neural Network) classifier and to train the histopathological behaviours such like BCP (Bags of chromatin Paches) and BNP (Bags of Nuclei Patches). In final layer, performance evaluation is done. A total number of 4520 nuclei observations were trained to construct the decision models from which BCP (Bags of Chromatin Patches) consists upon the 2650 and BNP (Bags of Nuclei Patches) comprises upon 1870 instances. Best measured accuracy for BCP was recorded as 97.93% and BNP accuracy was measured as 97.86%.

• Publication year

  2016

• Venue

  International Journal of Advanced Computer Science and Applications

• Publication date

  Unknown publication date

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.14569/IJACSA.2016.071135
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers.

  2015cited by this paper
• Fast Segmentation of Stained Nuclei in Terabyte-Scale, Time Resolved 3D Microscopy Image Stacks

  2014cited by this paper
• Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential

  2014cited by this paper
• Deep learning of feature representation with multiple instance learning for medical image analysis

  2014cited by this paper
• Anchored Neighborhood Regression for Fast Example-Based Super-Resolution

  2013cited by this paper
• Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

  2013cited by this paper
• CognitionMaster: an object-based image analysis framework

  2013cited by this paper
• A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching

  2013cited by this paper
• Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach

  2012cited by this paper
• Prediction of Malignancy in Suspected Thyroid Tumour Patients by Three Different Methods of Classification in Data Mining

  2012cited by this paper
• Quantitative Measurement for Thyroid Cancer Characterization Based on Elastography

  2011cited by this paper
• Super resolution using edge prior and single image detail synthesis

  2010cited by this paper
• Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns

  2010cited by this paper
• Discovery and identification of potential biomarkers of papillary thyroid carcinoma

  2009cited by this paper
• Nuclear pleomorphism scoring by selective cell nuclei detection

  2009cited by this paper
• Histopathological Image Analysis: A Review

  2009cited by this paper
• An automated distinction of DICOM images for lung cancer CAD system

  2009cited by this paper
• Image super-resolution as sparse representation of raw image patches

  2008influential reference
• Automated image analysis in histopathology: a valuable tool in medical diagnostics

  2008cited by this paper
• Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features

  2008cited by this paper
• Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer

  2006cited by this paper
• Vision Imaging and Image Processing for Early Cancer Diagnosis on Cytopathological Microscopy Images : Towards Fully Automatic AgNOR Detection

  2006cited by this paper
• The Bethesda System for Reporting Cervical/Vaginal Cytologic Diagnoses: Definitions, Criteria, and Explanatory Notes for Terminology and Specimen Adequacy

  1994cited by this paper
• The spatial distribution of exposed nuclear DNA in normal, cancer, and reverse-transformed cells.

  1990cited by this paper

• No citing papers are available for this paper.