Classification of White Blood Cells Using Modular Neural Network
AKSHAY A UMBARKAR
, DR. V. L. AGRAWAL
MatLab, Nuero Solution Software, Microsoft excel, Various Transform Techniques.
BLOOD tests are frequently employed to evaluate human health. One of the most straightforward blood tests is to quantify and identify the blood cell types. A complete blood count (CBC) is primarily a measure of these cellular components and is one of the most routinely ordered blood tests by clinicians. CBCs, especially white blood cell (WBC) count, provide physicians with key information valuable for diagnosing many different disease states including: anemia, leukemia, autoimmune disorders, fungal, and bacterial infections as well as Recognition and inspection of white blood cells in peripheral blood can assist hematologists in diagnosing many diseases such as AIDS, Leukemia, and blood cancer. Thus, this process is assumed as one of the most prominent steps in the hematological procedure. There are five main phases involved in the system. They are image pre-processing, extraction classifying and segmenting the Five Types of White Blood Cells. For classification neural classifiers in HISTOGRAM are used and also be using a more Efficient supervised learning approaches for more accurate and computationally efficient segmentation. The features are extracted from the Five Types of White Blood Cells using matlab program approach and these accurate features are used to train the neural classifier. Classification of Five Types of White Blood Cells is an essential research topic as it may be advantageous in monitoring many diseases. Therefore the need for fast, automatic, less expensive and accurate method to classify Five Types of White Blood Cells is of great realistic significance. The main aim of our project work to develop a Computer Aided diagnosed system for classification of Five Types of White Blood Cells.
"Classification of White Blood Cells Using Modular Neural Network", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.6, Issue 6, page no.256 - 261, June-2021, Available :https://ijsdr.org/papers/IJSDR2106039.pdf
Volume 6
Issue 6,
June-2021
Pages : 256 - 261
Paper Reg. ID: IJSDR_193432
Published Paper Id: IJSDR2106039
Downloads: 000347172
Research Area: Engineering
Country: Amravati, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
