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Abnormal and excessive cell growth, known as neoplasms, are not always a sign of cancer. These neoplasms may be benign malignant. Only malignant neoplasms are known as cancer. Therefore, determining the nature of a neoplasm is a critical step in the cancer detection process. Commonly used invasive methods for figuring out the type of neoplasm include fine needle aspiration cytology (FNAC) and tru-cut biopsy. Both these processes involve extracting tissue from the affected area via a needle and then observing the cells under a microscope. This observation is known in medical terms as histopathology. Histopathological images provide a direct evidence for the classification of the neoplasm. Histopathological image analysis however requires a skilled pathologist to describe the pathological findings. On the other hand, there are many non-invasive procedures used for cancer detection like elastography and mammography. Elastography maps the elastic properties of soft tissue using ultrasound or magnetic resonance imaging. Cancerous neoplasms will often be stiffer than healthier ones. A mammogram is just an x-ray picture of the breast. These non-invasive approaches unfortunately provide only an indirect evidence for classification making the classification job quite difficult. Creating a correlation between the non-invasive results and the histopathological images using deep learning can make it much easier to diagnose the nature of the tumor using only non-invasive procedures. Furthermore, training a deep network to recognize malignant tumors from histopathological images can make the entire cancer detection process automated. This paper proposes a deep learning based approach to determining the malignancy of a neoplasm using only non-invasive imaging. The results show good prediction accuracies.
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