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.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• In existing, proposed an approach for detection of 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 k means and Fuzzy c-means clustering for segmentation. They computed statistical, textural and geometrical features and applied Support Vector Machine (SVM) for classification.

• Their approach achieved 90% with Fuzzy c-means and 83% with k-means.

DISADVANTAGES OF EXISTING SYSTEM:

• Less Accuracy

• Time taken process

PROPOSED SYSTEM:

• In proposed system, we propose the design, development and evaluation of an automated system to accurately detect white blood cells cancer diseases. It detects types and sub-types of Leukemia (ALL and AML) and Myeloma.

• It is a recognition system applied on acquired blood microscopic images then performs preprocessing, segmentation, feature extraction and classification.

• Using the novel algorithm for detects all the sub types.

• To increase accuracy of detection and classification, use good machine learning models.

ADVANTAGES OF PROPOSED SYSTEM:

• The proposed solution converts images to YCBCR color space and construct Gaussian distribution of CB and CR values. Statistical, texture, size ratio and morphological features are then computed to train classifier.

• Unlike existing systems, 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.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System            : Pentium Dual Core.

• Hard Disk        : 120 GB.

• Monitor           : 15’’ LED

• Input Devices  : Keyboard, Mouse

• Ram                 : 1GB.

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.

• Coding Language : MATLAB.

• Tool                       : MATLAB R2013A

REFERENCE:

Hend Mohamed, Rowan Omar, Nermeen Saeed, Ali Essam, Nada Ayman, Taraggy Mohiy and Ashraf AbdelRaouf, “Automated Detection of White Blood Cells Cancer Diseases”, IEEE 2018.