Algorithm A1: Pseudocode of the Proposed Model Working Principle.

Input: Input image set (D), total pixel (TP), Gray Level intensity (8/16/24), number of images (I) Output: Cancer Relapse, Cancer Non-Relapse Procedure: Initiate the image preprocessing phase-1 to calculate the blank ratio of an image from the dataset. For t 1←I Find the number of Blank Pixels (B)   Find the TP   Find the Blank Ratio (BR) as ${\displaystyle \frac{B}{TP}}\times 100\%$   If (BR > 30%)     Discard the image   Else     Keep the image   EndIf   Return D’ with a set of images having BR < 30% EndFor Initiate the preprocessing phase-2 (CLAHE) to D’ for enhancing the intensity level contrast Divide the original image into small, non-overlapping tiles (ST) For t 1←ST   Calculate the Cumulative distribution function (C) using Equation (1).   Clip the C to limit (${Clip}_{max}$) the contrast level for the current intensity   Return image i EndFor Form update the dataset D’ with enhanced Contrast level Apply WMCM to D’. For t ← 1 to I Divide the image into sub-bands $SB\in \{LL,LH,HL, HH\}$ For k ← 1 to |SB|   Find GLCM for each intensity (i,j) of the image     ${GLCM}_{SB}\left(i,j\right)=\sum _{m=1}^M\sum _{n=1}^N\left\{\begin{array}{c}1, if H\left(a,b\right)=i ,H\left(a^{\prime },b^{\prime }\right)=j\\ 0, Otherwise\end{array}\right.$   EndFor   $WMCM(i,j)=\sum _{k=1}^K{GLCM}_{{SB}_k}\left(i,j\right)$ EndFor Initiate Texture feature extraction from WMCM For t ← 1 to I   Calculate $SGSDA, SGBDA, GLA, DLA, GLMSE, DLMSE, C, Co, E, En,H$   Calculate Texture Feature (${TF}_{i,j}$) for intensity (i,j) EndFor Find TF by combining calculated ${TF}_{i,j}$ Split the TF with test size 0.2 Apply Base Learners SVM, LR, DT, RF, AdaBoost, and XGBoost to form an initial prediction. Apply Weighted Averaging, Hard Voting, and Soft Voting to form ensemble models. Evaluate the trained ensemble model over Test Data Cancer Classification.