Adv of automated detection of white blood cells cancer diseases
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Automated Detection of White Blood Cells Cancer Diseases
ABSTRACT
Automated diagnosis of white blood cells cancer diseases such as Leukemia and Myeloma is a challenging biomedical research topic. Our approach presents for the first time a new state of the art application that assists in diagnosing the white blood cells diseases. We divide these diseases into two categories, each category includes similar symptoms diseases that may confuse in diagnosing. Based on
the doctor’s selection, one
of two approaches is implemented. Each approach is applied on one of the two diseases category by computing different features. Finally, Random Forest classifier is applied for final decision. The proposed approach aims to early discovery of white blood cells cancer, reduce the misdiagnosis cases in addition to improve the system learning methodology. Moreover, allowing the experts only to have the final tuning on the result obtained from the system. The proposed approach achieved an accuracy of 93% in the first category and 95% in the second category
.
BLOCK DIAGRAM
Input ImagePreprocessingSegmentationFeature Extractionclassification
EXISTING SYSTEM PROPOSED SYSTEM
EXISTING CONCEPT:
Our system has the ability of learning from misclassified tests to enhance the future accuracy of the system. Random Forest classifier is the best classifier that is able to differentiate between different types and the one which gives us the best accuracy. The system achieved 94.3 % accuracy in detecting and classifying types and sub-types
.
PROPOSED CONCEPT:
Leukemia in blood at early stages. They have used adaptive median filter for noise removal and adaptive Histogram Equalization for contrast enhancement in preprocessing stage. They applied kmeans and Fuzzy c-means clustering for segmentation. They computed statistical, textural and geometrical features and applied Support Vector Machine (SVM) for classification. Automatically detects and segments AML in blood smears. Segmentation was done in the CIELAB Color space by K-Means clustering algorithm.
EXISTING TECHNIQUE :
Gustafson Kessel Clustering
PROPOSED ALGORITHM:
Watershed Algorithm,
K-Means Clustering Algorithm
TECHNIQUE DEFINITION:
They applied selective median filtering followed by unsharp masking in preprocessing. Isegmentation, they used improved version of fuzzy clustering technique viz. Gustafson Kessel clustering followed by nearest neighbor classification in L*a*b* color space (L* for lightness, a* for redness greenness axis, and b*a yellowness blueness axis). The computed features are two novel shape features; Hausdorff Dimension and contour signature.
ALGORITHMDEFINITION:
An approach by Agaian et al. [16] proposed a simple technique that automatically detects and segments AML in blood smears. Segmentation was done in the CIELAB Color space by K-Means clustering algorithm isolating the blood cell and the cell nucleus. They computed area, perimeter, circularity and form factor features. Gaussian Mixture Models (GMM) and Binary.