Recent advances in deep learning have often surpassed human perfor-mance in image classification. Among the most renowned cases, just think of the ImageNet Large Scale Visual Recognition Challenge competition. However, challenges persist in complex fields such as medical imaging. An example is the Human Protein Atlas which maps all human proteins in more than 171,000 im-ages that makes a computation challenge due to high class imbalance. To address these challenges from a green perspective, we propose a transfer learning ap-proach using Convolutional Neural Networks (CNNs) pre-trained on the ImageNet dataset. We use CNN layers as feature extractors, feeding the extracted features into a Support Vector Machine with a linear kernel. Our method com-bines both image-level and cell-level perspectives. At the cell level, we segment nuclei and extract the surrounding nuclear membrane area. The ensemble classi-fication shows promising performance with limited computational effort.