LAUSR

One of the most dangerous cancer is acute leukemia lymphoblastic (ALL). This type of leukemia has been attacking the most of children. This research aims to detect the ALL diseases through the principal features of the object. Furthermore, this paper contributes by providing a new approach to detect the ALL disease based on the Color Orthonormal Basis Entropy (COBE) and the Distribution of the Pixel Intensity (DoPI). There are some novelties in this research. Firstly, we combined three channels features to characterize an object for increasing the performance accuracy. Secondly, we have developed a model to improve the image through a contrast enhancement filter, the noises from the image can be recognized and removed automatically, and better image result has been delivered through enhancement of the green channel selection. In this research, we proposed a novel approach to detect the ALL based on the COBE and the DoPI. This new approach is divided into four main stages, i.e., image enhancement, segmentation, feature extraction, and performance measurements. Firstly, an image was enhanced through a channel separation, enhancement of the green channel, and fusion of the green channel enhancement with the original channel, i.e., the red and blue channels. An image enhancement process is conducted by a gamma correction of the contrast stretching. The hue channel is an output of the first stage. Secondly, the Otsu algorithm was employed to obtain the best threshold. Thirdly, the COBE and the DoPI were included to discover the main features. Lastly, the Manhattan method was employed to measure performance. In this research, the second Acute Lymphoblastic Leukemia-Image Database (The 2nd ALL-IDB, usually it is called as ALL-IDB2). It is carried out to evaluate the proposed approach. The results show this proposed method has produced high accuracy at 91.67% and small standard deviation at 0.022.