Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

Table 2

Comparison of the performance of deep learning-based classification methods.

Publication	Type of images	Proposed methods		Comparison baseline
		Method	Results	Method	Results
Reda et al. [40]	DW-MRI	SNCAE	ACC = 1, SEN = 1, SPE = 1		ACC = 0.943, SEN = 0.943, SPE = 0.944
Reda et al. [41]	DW-MRI	SNCAE	ACC = 1, SEN = 1, SPE = 1, AUC ≈ 1		ACC = 0.943, SEN = 0.962, SPE = 0.926, AUC = 0.93
Zhu et al. [42]	T2-weighted, DWI and ADC	SAE	SBE = 0.8990 ± 0.0423, SEN = 0.9151 ± 0.0253, SPE = 0.8847 ± 0.0389	HOG features	SBE = 0.8814 ± 0.0534, SEN = 0.9191 ± 0.0296, SPE = 0.8696 ± 0.0563
Akkus et al. [43]	T1-postcontrast (T1C) and T2	Multiscale CNN	ACC = 0.877, SEN = 0.933, SPE = 0.822	—	—
Pan et al. [44]	BRATS 2014	CNN	SEN = 0.6667, SPE = 0.6667	NN	SEN = 0.5677, SPE = 0.5677
Hirata et al. [45]	FDG PET	CNN	ACC = 0.88		ACC = 0.80
Hirata et al. [46]	MET PET	CNN	ACC = 0.888 ± 0.055		ACC = 0.66
Teramoto et al. [47]	PET/CT	CNN	SEN = 0.901, with 4.9 FPs/case	Active contour filter	SEN = 0.901, with 9.8 FPs/case
Wang et al. [48]	FDG PET	CNN	ACC = 0.8564 ± 0.0809, SEN = 0.8353 ± 0.1385, SPE = 0.8775 ± 0.1030 AUC = 0.9086 ± 0.0865	AdaBoost + D13	ACC = 0.8505 ± 0.0897, SEN = 0.8565 ± 0.1346, SPE = 0.8445 ± 0.1261 AUC = 0.9143 ± 0.0751
Antropova et al. [51]	DCE-MRI	CNN ConvNet	AUC = 0.85	—	—

Notes. DW-MRI = diffusion-weighted magnetic resonance images; SNCAE = stacked nonnegativity-constrained autoencoders; ACC = accuracy; SEN = sensitivity; SPE = specificity; AUC = area under the receiver operating characteristic curve; = -Star, a classifier implemented in Weka toolbox [59]; DWI = diffusion-weighted imaging; ADC = apparent diffusion coefficient; SAE = stacked autoencoder; SBE = section-based evaluation; HOG = histogram of oriented gradient; CNN = convolutional neural network; BRATS = multimodal brain tumor segmentation dataset, including four MRI sequences (T1W, T1-postcontrast, T2W, and FLAIR); NN = neural network; FDG = fluorodeoxyglucose; PET = positron emission tomography; = maximum standardized uptake value; MET = ¹¹C-methionine; CT = computed tomography; FP = false positive; AdaBoost = adaptive boosting; D13 = 13 diagnostic features.