Research Article
Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering
Table 3
Performance criteria of the proposed method applied on high-grade/low-grade tumor images from INNN: Dice, Jaccard, sensitivity, and specificity score.
| ID | Dice | Jaccard | Sensitivity | Specificity |
| INNN001 | 0.942 | 0.851 | 0.943 | 1 | INNN002 | 0.899 | 0.785 | 0.926 | 1 | INNN003 | 0.907 | 0.839 | 0.938 | 1 | INNN004 | 0.860 | 0.763 | 0.918 | 1 | INNN005 | 0.968 | 0.882 | 0.978 | 1 | INNN006 | 0.801 | 0.701 | 0.880 | 1 | INNN007 | 0.795 | 0.699 | 0.834 | 1 | INNN008 | 0.807 | 0.731 | 0.890 | 1 | INNN009 | 0.861 | 0.725 | 0.914 | 1 | INNN010 | 0.842 | 0.711 | 0.906 | 1 | INNN011 | 0.828 | 0.701 | 0.886 | 1 | INNN012 | 0.837 | 0.707 | 0.901 | 1 | INNN013 | 0.871 | 0.765 | 0.933 | 1 | INNN014 | 0.933 | 0.831 | 0.940 | 1 | INNN015 | 0.911 | 0.876 | 0.939 | 1 | INNN016 | 0.907 | 0.855 | 0.927 | 1 | INNN017 | 0.732 | 0.658 | 0.811 | 1 | INNN018 | 0.790 | 0.684 | 0.839 | 1 | INNN019 | 0.801 | 0.713 | 0.846 | 1 | INNN020 | 0.951 | 0.873 | 0.967 | 1 | INNN021 | 0.511 | 0.440 | 0.816 | 0.901 | INNN022 | 0.887 | 0.778 | 0.949 | 1 | INNN023 | 0.819 | 0.718 | 0.850 | 1 | INNN024 | 0.918 | 0.877 | 0.942 | 1 | INNN025 | 0.851 | 0.728 | 0.944 | 1 | Mean | 0.850 | 0.756 | 0.905 | 0.996 | Standard deviation | 0.091 | 0.097 | 0.048 | 0.019 |
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