4. DL-based systems in clinical diagnosis
Several studies observed that DL improved accuracy in diagnosis and
clinical assessment. The DL architectures performed well, especially in
cardiovascular MRI (CMR), fatty liver disease (FLD) tissue
characterization and rheumatic arthritis. Although these are separate
areas of medical science where different modalities are used, the
success of DL motivates the expansion of its horizon. Moreover, the
success of DL-based strategy in anatomical research inspire application
in CVD diagnosis and prediction. Schmauch et al. proposed a
DenseNet-based supervised learning system for the detection
classification of focal liver lesions (FLL) [124]. The authors
demonstrated region-of-convergence (ROC) and area under the curve (AUC)
scores of 0.935 and 0.916 for lesion detection and classification on 367
ultrasound images [124]. Huynh et al. [125] applied transfer
learning for detecting malignant tumours. Features from pre-trained
AlexNet were used for training. The network had performance similar to
analytical methods (AUC = 0.81) [125]. Hatipoglu et al. performed
cell segmentation using three DL-based methods: CNN, DBN and
autoencoder. The comparative study reported that CNN and autoencoders
outperformed conventional segmentation techniques [126].
Table III Benchmarking deep learning-based studies in clinical
diagnosis.