Subsequently, a famous ML model, the VGG16 model, was trained to classify the fused images. The VGG16 algorithm is a widely used CNN model, which has been shown to perform well on a broad range of image classification tasks. The VGG16 model has 16 convolution layers, and each of them is followed by a maximum-pooling layer. After max pooling, the processed image representation is passed through three fully connected layers (FC) to predict the label ("good quality" or "poor quality"). The details of the model architecture and the training process can be found in the SI-4. One of the indicators of evaluating the ML model is the accuracy of classification of images with good or poor quality. After optimization, the proposed method using our model architecture was able to classify the SECM images well with very high accuracy as high as 97%. Figure demonstrates the overall approach of AI-assisted image fusion for revealing cancerous areas according to the biomarker distribution.