Deep Learning Based Approach for Multiple Myeloma Detection

Multiple myeloma cancer is caused by the abnormal growth of plasma cells in the bone marrow. The most commonly used method for diagnosis of multiple myeloma is Bone marrow aspiration, where the aspirate slide images are either observed visually or passed onto existing digital image processing software for the detection of myeloma cells. The current work explores the effectiveness of deep learning based object detection/segmentation algorithms such as Mask-RCNN and unet for the detection of multiple myeloma. The manual polygon annotation of the current dataset is performed using VGG image annotation software. The deep learning models were trained by monitoring the train and validation loss per epoch and the best model was selected based on the minimal loss for the validation data. From the comparison results obtained for both the models, it is observed that Mask-RCNN has competing results than unet and it addresses most of the challenges existing in multiple myeloma segmentation.