## 3 Methods
### Feature Extraction Module (FEM)
A two-dimensional Gabor function is made up of a sinusoidal plane wave with some frequencies and orientations that are modulated by a two-dimensional Gaussian envelope. In the spatial domain, the 'canonical' Gabor filter is given by [19]:  
\[H=\mathit{Cos}^{-1}\quad\frac{0.5[(R-G)+(R-B)]}{\sqrt{(R-G)\ ^{2}+(R-G)(R-B)}} \tag{1}\]0  
where \(\phi\) is the phase of the sinusoidal plane wave along the z-axis, and \(\sigma_{x}\) is the space constants of the Gaussian envelope along the z-axis. A Gabor filter with arbitrary orientation, \(\theta_{0}\), was obtained via a rigid rotation of the x-y coordinate system. These two-dimensional functions were shown to be good fits for the receptive field profiles of simple cells in the striate cortex. A Gabor filter's frequency and orientation-selective properties are more explicit in its frequency domain representation. The Fourier domain representation specifies how much each frequency component of the input image is modified or modulated by the filter. As a result, such representations are known as modulation transfer functions (MTF) [20]. The following step is to perform the discrete wavelet transform for extracting the moments. The discrete wavelet transform provides more information and is more adaptable. It divides data into frequency components or sub-bands. The wavelet has an advantage over Fourier in analyzing physical situations because the sinusoid does not have a fixed duration but instead extends from minus to plus infinity. Most audio signal loses information in the Fourier transform domain [21,22]. A wavelet expansion coefficient is a local and easy to-understand component. Wavelets are adjustable, adaptable, and intended for use in adaptive systems, whereas the Fourier transform is appropriate when the signal contains only a few stationary components [22]. There are two types of features as shown in \[H=\mathit{Cos}^{-1}\quad\frac{0.5[(R-G)+(R-B)]}{\sqrt{(R-G)\ ^{2}+(R-G)(R-B)}} \tag{1}\]1  
Texture provides statistics on the spatial arrangement of intensities in an image. Leukemia causes significant changes in the chromatin distribution of WBC nuclei, which can be visualized as texture. Changes in chromatin distribution reflect the organization of DNA in the nucleus and are an important diagnostic descriptor for distinguishing benign from malignant cells. The texture feature descriptor grey level co-occurrence matrix (GLCM) was used to determine the shape and texture parameters of a nucleus. Second-order statistics, such as the probability of two pixels having specific grey levels at specific spatial relationships, was used to describe grey-level pixel distribution. These data can be represented in 2-dimensional grey level co-occurrence matrices, which can be computed for different distances and orientations. Statistical measures that have been used to extract textual characteristics from the GLCM have been defined. Some of these characteristics are as follows [23; 24]:  
1. Energy: a measure of image homogeneity. 2. Contrast: a different moment of the regional co-occurrence matrix that measures the contrast or the number of local variations in an image. 3. Correlation: a measure of the image's regional pattern liner dependencies. 4. Homogeneity: returns a value indicating how close the distribution of elements in the GLCM is to the diagonal GLCM. In addition to the GLCM features, the statistical features can be described as [24; 25; 26]:  
1. Entropy: the measure of randomness or disorder in the images. 2. Mean: measure of the image's brightness by calculating the average value of pixels inside the region of interest. 3. Standard deviation: used for deciding what is normal, extra-large, or extra-small. 4. RMS: the root mean square. 5. Smoothness: a measurement of grey level disparity that can be used to create relative smoothness recipes. 6. Kurtosis: measure of the peak of the distribution of the intensity values around the mean. 7. Skewness: assesses the absence of symmetry.