Hematoxylin and Eosin (H&E) images, popularly used in the field of digital pathology, often pose challenges due to their limited color richness, hindering the differentiation of subtle cell features crucial for accurate classification. Enhancing the visibility of these elusive cell features helps train robust deep-learning models. However, the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community. To address this challenge, we introduce Salient Features Guided Augmentation (SFGA), an approach that strategically integrates machine learning and image processing. SFGA utilizes machine learning algorithms to identify crucial features within cell images, subsequently mapping these features to appropriate image processing techniques to enhance training images. By emphasizing salient features and aligning them with corresponding image processing methods, SFGA is designed to enhance the discriminating power of deep learning models in cell classification tasks. Our research undertakes a series of experiments, each exploring the performance of different datasets and data enhancement techniques in classifying cell types, highlighting the significance of data quality and enhancement in mitigating overfitting and distinguishing cell characteristics. Specifically, SFGA focuses on identifying tumor cells from tissue for extranodal extension detection, with the SFGA-enhanced dataset showing notable advantages in accuracy. We conducted a preliminary study of five experiments, among which the accuracy of the pleomorphism experiment improved significantly from 50.81% to 95.15%. The accuracy of the other four experiments also increased, with improvements ranging from 3 to 43 percentage points. Our preliminary study shows the possibilities to enhance the diagnostic accuracy of deep learning models and proposes a systematic approach that could enhance cancer diagnosis, contributing as a first step in using SFGA in medical image enhancement.