Creating a Hybrid model for the classification of lung, colon, and breast cancer using histopathological images.

The project is about creating a Hybrid model for the classification of lung, colon, and breast cancer using histopathological images. There are two datasets one dataset contains lung and colon cancer histopathological images and the other dataset contains breast cancer histopathological images. The histopathological images in the lung and colon dataset must be converted from bgr to rbg. The dataset needs to be divided into 80% training and 20% testing. The CNN model that will be used will be ResNet-50. I need someone who knows how to do the training (/labeling if required) for the dataset and can write the python code for the project using google colab. The output will be when I give the code an image it tells me if the given image is lung, colon, or breast and if it is benign or malignant ( cancerous or non-cancerous), and from which class of benign or malignant is it via text. In Addition, you need to plot the following graphs training, testing, performance, Precision, Error/Loss, F1-score, Confusion Matrix, ROC curve, and the AUC score. Lastly, the model accuracy must be at least 98% preferable to 99%.