Magnification independent fine-tuned transfer learning adaptation for multi-classification of breast cancer in histopathology images

Breast cancer is the prominent foremost disease causing death among the various cancers affecting female. Precise and initial diagnosis of diseases can often save lives. Histopathology microscopy imaging as a best practice used by pathologist to diagnose breast cancer. The procedure is labor intensive and time consuming. Hence, investigation of tissue samples in automated manner from histopathology images has constructive clinical implication in early diagnosis and treatment of disease. To capture discriminating traits from images in better way, images are acquired at various optical magnifications (x40, x100, x200 and x400). The disease can be treated well if the type of actual subclass of benign and malignant categories on a sample image is known early. The research gap observed in current literature isthat, limited study done on magnification independent multi- classification. With recent advances in Artificial Intelligence (AI)especially deep learning, the Convolutional Neural Networks (CNN) can be effectively utilized for automatic histopathology image analysis. In this work, multi-classification framework across different magnification factor using recent tax- onomy of BreakHis dataset is proposed. The proposed approach explores different transfer learning models with fine tuning adaptation. The experiments have shown impressive results on pre-trained models DenseNet121 as well as on DenseNet201. Promising results are observed towards multi-classification of breast cancer histopathology images as compared to other models.