Hardware Deployable Classifier for Invasive Ductal Carcinoma using Tiny Machine Learning Model

Breast Cancer is a global health concern, emphasizing the crucial importance of its early detection in improving patient treatment. This is especially true for the most frequently diagnosed type of breast cancer, Invasive Ductal Carcinoma (IDC), which has a high risk of spreading and significant mortality. Automation provides an opportunity to streamline the detection, improving efficiency and minimizing diagnostic errors. In this research paper, we proposed a low-computational Tiny Machine Learning (TinyML) model for the classification of IDC. Conventional methods for IDC classification, such as histopathological analysis and mammography, are time-consuming, costly, and subjective. Therefore, there is a need for an automated and accurate method for IDC classification that can be applied in a resource-limited environment. Our proposed TinyML model takes digital images of whole-mount breast cancer slide specimens and extracts the features necessary to classify them as IDC-(-ve) or IDC-(+ve). The TinyML model is evaluated using a publicly available dataset of Breast Histopathology Images and compared to the other Deep Learning (DL) models based on accuracy and computational complexity. The results show that our model achieved a high accuracy of 89.3% while maintaining limited processing power and memory. The successful deployment of the model on an embedded system like Raspberry Pi demonstrates that our TinyML model can be used in hardware resource- constrained environments, promising improved cancer diagnosis.