End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

Table 2

Comparison with the state-of-the-art baselines on dataset.

Models		Accuracy	Sensitivity	Specificity	F1-score
AlexNet (2012) [43]	Normal	0.868	0.785	0.785	0.878
	Osteopenia	0.799	0.793	0.793	0.687
	Osteoporosis	0.931	0.882	0.882	0.756
VGG-19 (2014) [44]	Normal	0.856	0.994	0.765	0.847
	Osteopenia	0.756	0.795	0.655	0.825
	Osteoporosis	0.900	0.894	0.941	0.939
GoogLeNet (2015) [45]	Normal	0.899	0.976	0.849	0.886
	Osteopenia	0.811	0.871	0.655	0.869
	Osteoporosis	0.911	0.902	0.980	0.947
ResNet-50 (2016) [41]	Normal	0.911	0.874	0.936	0.888
	Osteopenia	0.868	0.894	0.802	0.907
	Osteoporosis	0.956	0.995	0.986	0.976
ResNet-101 (2016) [41]	Normal	0.938	0.958	0.924	0.925
	Osteopenia	0.871	0.917	0.750	0.911
	Osteoporosis	0.933	0.940	0.882	0.961
DenseNet-121 (2017) [46]	Normal	0.897	0.982	0.840	0.884
	Osteopenia	0.849	0.841	0.871	0.889
	Osteoporosis	0.952	0.967	0.843	0.972
ShuffleNet (2018) [47]	Normal	0.926	0.970	0.896	0.913
	Osteopenia	0.856	0.911	0.716	0.902
	Osteoporosis	0.931	0.924	0.980	0.959
EfficientNet (2019) [48]	Normal	0.926	0.976	0.892	0.913
	Osteopenia	0.871	0.904	0.784	0.910
	Osteoporosis	0.945	0.943	0.961	0.968
Joint framework (ours)	Normal	0.971	0.964	0.976	0.964
	Osteopenia	0.933	0.970	0.836	0.954
	Osteoporosis	0.957	0.962	0.922	0.975

The bold value indicates that this is the best model results.