RadGenNets: Deep Learning-Based Radiogenomics Model For Gene Mutation Prediction In Lung Cancer

Abstract

In this paper, we present our methodology that can use for predicting gene mutation in patients with non-small cell lung cancer (NSCLC). There are three major types of gene mutations that an NSCLC patient's gene structure can change epidermal growth factor receptor (EGFR), Kirsten rat sarcoma virus (KRAS), and Anaplastic lymphoma kinase (ALK). We worked with the clinical and genomics data for each patient as well CT scans. We preprocessed all of the data and then built a novel pipeline to integrate both the image and tabular data. We built a novel pipeline that used a fusion of Convolutional Neural Networks and Dense Neural Networks. Also, using a search approach, we pick an ensemble of deep learning models to classify the separate gene mutations. These models include EfficientNets, SENet, and ResNeXt WSL, among others. Our model achieved a high area under curve (AUC) score of 94% in detecting gene mutation.

HematoNet: Expert Level Classification of Bone Marrow Cytology Morphology in Hematological Malignancy with Deep Learning

“…Convolutional neural networks have helped to significantly increase the accuracy of computer vision classification tasks in the last few years [30, 31, 32, 33, 34, 35]. These methods has also been used to identify multiple types of cancer, predict progression of tumor, and classify various types of skin diseases [36, 37, 38, 39, 40, 41]. Because of this, the effective use of CNNs for image classification depends on the availability of a substantial quantity of image data and high-quality annotation, which may be difficult to achieve because of the cost associated in collecting labels by medical specialists [42, 43, 44, 45].…”