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Table 3 Classification performance on Epistroma dataset

From: Texture features in the Shearlet domain for histopathological image classification

Method

Perf

RP + Magnitude

RP

Magnitude

SVM

DTB

SVM

DTB

SVM

DTB

(1): Baseline performance of existing Shearlet-based methods

Vo et al.

ACC%

\(\underline{96} .\underline{44} \% \pm \underline{1} .\underline{47} \%\)

\(95.42\% \pm 1.03\%\)

\(95.50\% \pm 1.52\%\)

\(94.26\% \pm 1.95\%\)

\(94.48\% \pm 1.54\%\)

\(94.26\% \pm 1.35\%\)

AUC

\(\underline{0} .\underline{9895} \pm \underline{0} .\underline{0082}\)

\(0.9897 \pm 0.0047\)

\(0.9873 \pm 0.0078\)

\(0.9839 \pm 0.0087\)

\(0.9830 \pm 0.0083\)

\(0.9763 \pm 0.0102\)

Sen

\(\underline{0} .\underline{9685} \pm \underline{0} .\underline{0131}\)

\(0.9443 \pm 0.0162\)

\(0.9685 \pm 0.0117\)

\(0.9467 \pm 0.0268\)

\(0.9588 \pm 0.0182\)

\(0.9564 \pm 0.0152\)

Prec

\(\underline{0} .\underline{9722} \pm \underline{0} .\underline{0170}\)

\(0.9787 \pm 0.0083\)

\(0.9577 \pm 0.0260\)

\(0.9576 \pm 0.0220\)

\(0.9501\pm 0.0219\)

\(0.9487 \pm 0.0193\)

Meshkini and Ghassemian

ACC%

\(\textit{96.08}\% \pm \textit{1.24}\%\)

\(\textit{96.29}\% \pm \textit{1.86}\%\)

\(\textit{95.35}\% \pm \textit{1.37}\%\)

\(\textit{96.37} \% \pm \textit{2.30}\%\)

\(95.35\% \pm 1.49\%\)

\(96.44 \% \pm 2.20\%\)

AUC

\(\textit{0.9926} \pm \textit{0.0046}\)

\(\textit{0.9929} \pm \textit{0.0041}\)

\(\textit{0.9887} \pm \textit{0.0058}\)

\(\textit{0.9919} \pm \textit{0.0077}\)

\(0.9899 \pm 0.0050\)

\(0.9908 \pm 0.0068\)

Sen

\(\textit{0.9710} \pm \textit{0.0190}\)

\(\textit{0.9600} \pm \textit{0.0222}\)

\(\textit{0.9685} \pm \textit{0.0207}\)

\(\textit{0.9600} \pm \textit{0.0269}\)

\(0.9722 \pm 0.0161\)

\(0.9564 \pm 0.0274\)

Prec

\(\textit{0.9642} \pm \textit{0.0169}\)

\(\textit{0.9778} \pm \textit{0.0151}\)

\(\textit{0.9550} \pm \textit{0.0183}\)

\(\textit{0.9791} \pm \textit{0.0184}\)

\(0.9517 \pm 0.0186\)

\(0.9840 \pm 0.0181\)

Zhou et al.

ACC%

\(\textit{89.68}\% \pm \textit{1.33}\%\)

\(\textit{88.30}\% \pm \textit{2.68}\%\)

\(\textit{81.69}\% \pm \textit{2.53}\%\)

\(\textit{79.65} \% \pm \textit{3.11}\%\)

\(84.45\% \pm 2.98\%\)

\(87.13 \% \pm 2.95\%\)

AUC

\(\textit{0.9506} \pm \textit{0.0158}\)

\(\textit{0.9400} \pm \textit{0.0223}\)

\(\textit{0.8748} \pm \textit{0.0222}\)

\(\textit{0.8721} \pm \textit{0.0367}\)

\(0.9110 \pm 0.0194\)

\(0.9416 \pm 0.0244\)

Sen

\(\textit{0.9370} \pm \textit{0.0219}\)

\(\textit{0.9103} \pm \textit{0.0287}\)

\(\textit{0.8934} \pm \textit{0.0363}\)

\(\textit{0.8666} \pm \textit{0.0343}\)

\(0.9236 \pm 0.0141\)

\(0.9102 \pm 0.0327\)

Prec

\(\textit{0.8961} \pm \textit{0.0183}\)

\(\textit{0.8968} \pm \textit{0.0275}\)

\(\textit{0.8186} \pm \textit{0.0251}\)

\(\textit{0.8088} \pm \textit{0.0301}\)

\(0.8357 \pm 0.0330\)

\(0.8799 \pm 0.0282\)

Dong et al.

ACC%

\(\textit{95.72}\% \pm \textit{1.54}\%\)

\(\textit{95.28}\% \pm \textit{1.66}\%\)

\(\textit{94.84}\% \pm \textit{1.59}\%\)

\(\textit{93.39}\% \pm \textit{2.11}\%\)

\(95.86\% \pm 1.32\%\)

\(93.75\% \pm 1.15\%\)

AUC

\(\textit{0.9882} \pm \textit{0.0071}\)

\(\textit{0.9837} \pm \textit{0.0089}\)

\(\textit{0.9836} \pm \textit{0.0071}\)

\(\textit{0.9776} \pm \textit{0.0132}\)

\(0.9884 \pm 0.0082\)

\(0.9836 \pm 0.0101\)

Sen

\(\textit{0.9551} \pm \textit{0.0162}\)

\(\textit{0.9503} \pm \textit{0.0271}\)

\(\textit{0.9503} \pm \textit{0.0218}\)

\(\textit{0.9187} \pm \textit{0.0300}\)

\(0.9685 \pm 0.0103\)

\(0.9600 \pm 0.0182\)

Prec

\(\textit{0.9735} \pm \textit{0.0227}\)

\(\textit{0.9707} \pm \textit{0.0178}\)

\(\textit{0.9637} \pm \textit{0.0211}\)

\(\textit{0.9697} \pm \textit{0.0201}\)

\(0.9630 \pm 0.0194\)

\(0.9381 \pm 0.0231\)

(2): Proposed Shearlet-Based Methods For Textured Bio-medical Image Classification

CM

ACC%

\(\underline{97} .\underline{24} \% \pm \underline{1} .\underline{27} \%\)

\(94.41\% \pm 1.48\%\)

\(96.66\% \pm 1.14\%\)

\(94.99\% \pm 1.43\%\)

\(96.00\% \pm 1.30\%\)

\(94.19\% \pm 2.19\%\)

AUC

\(\underline{0} .\underline{9917} \pm \underline{0} .\underline{0067}\)

\(0.9819 \pm 0.0126\)

\(0.9925 \pm 0.0064\)

\(0.9828 \pm 0.0116\)

\(0.9863 \pm 0.0087\)

\(0.9828 \pm 0.0091\)

Sen

\(\underline{0} .\underline{9733} \pm \underline{0} .\underline{0126}\)

\(0.9419 \pm 0.0258\)

\(0.9672 \pm 0.0129\)

\(0.9503 \pm 0.0134\)

\(0.9636 \pm 0.0163\)

\(0.9418 \pm 0.0294\)

Prec

\(\underline{0} .\underline{9807} \pm \underline{0} .\underline{0150}\)

\(0.9646 \pm 0.0202\)

\(0.9769 \pm 0.0130\)

\(0.9658 \pm 0.0175\)

\(0.9696 \pm 0.0116\)

\(0.9607 \pm 0.0186\)

LBP

ACC%

\(95.64\% \pm 1.32\%\)

\(95.57\% \pm 2.04\%\)

\(95.50\% \pm 1.06\%\)

\(94.04\% \pm 1.56\%\)

\(95.71\% \pm 1.39\%\)

\(93.90\% \pm 1.75\%\)

AUC

\(0.9890 \pm 0.0073\)

\(0.9905 \pm 0.0069\)

\(0.9871 \pm 0.0076\)

\(0.9850 \pm 0.0054\)

\(0.9876 \pm 0.0088\)

\(0.9800 \pm 0.0113\)

Sen

\(0.9624 \pm 0.0146\)

\(0.9479 \pm 0.0261\)

\(0.9661 \pm 0.0178\)

\(0.9394 \pm 0.0221\)

\(0.9600 \pm 0.0153\)

\(0.9406 \pm 0.0239\)

Prec

\(0.9653 \pm 0.0203\)

\(0.9779 \pm 0.0210\)

\(0.9594 \pm 0.0133\)

\(0.9607 \pm 0.0176\)

\(0.9687 \pm 0.0206\)

\(0.9573 \pm 0.0194\)

LOSIB

ACC%

\(96.29\% \pm 1.47\%\)

\(95.13\% \pm 1.61\%\)

\(95.50\% \pm 1.32\%\)

\(96.65\% \pm 1.51\%\)

\(95.35\% \pm 1.85\%\)

\(93.10\% \pm 1.29\%\)

AUC

\(0.9900 \pm 0.0069\)

\(0.9870 \pm 0.0074\)

\(0.9868 \pm 0.0073\)

\(0.9911 \pm 0.0065\)

\(0.9892 \pm 0.0087\)

\(0.9825 \pm 0.0103\)

Sen

\(0.9733 \pm 0.0096\)

\(0.9454 \pm 0.0245\)

\(0.9721 \pm 0.0152\)

\(0.9624 \pm 0.0218\)

\(0.9758 \pm 0.0141\)

\(0.9503 \pm 0.0276\)

Prec

\(0.9656 \pm 0.0215\)

\(0.9729 \pm 0.0179\)

\(0.9541 \pm 0.0203\)

\(0.9816 \pm 0.0132\)

\(0.9487 \pm 0.0239\)

\(0.9362 \pm 0.0194\)

SFTA

ACC%

\(95.71\% \pm 1.54\%\)

\(93.90\% \pm 1.81\%\)

\(95.28\% \pm 2.16\%\)

\(91.57\% \pm 1.91\%\)

\(94.33\% \pm 1.52\%\)

\(92.01 \% \pm 2.07\%\)

AUC

\(0.9881 \pm 0.0064\)

\(0.9841 \pm 0.0106\)

\(0.9870 \pm 0.0068\)

\(0.9775 \pm 0.0093\)

\(0.9824 \pm 0.0080\)

\(0.9732 \pm 0.0125\)

Sen

\(0.9661 \pm 0.0137\)

\(0.9297 \pm 0.0282\)

\(0.9624 \pm 0.0145\)

\(0.9006 \pm 0.0265\)

\(0.9636 \pm 0.0114\)

\(0.9455 \pm 0.0221\)

Prec

\(0.9628 \pm 0.0169\)

\(0.9676 \pm 0.0184\)

\(0.9593 \pm 0.0233\)

\(0.9572 \pm 0.0266\)

\(0.9438 \pm 0.024\)

\(0.9232 \pm 0.0178\)

(3): Integrating Shearlet-based existing techniques with our proposed methods

Fusion #1

ACC%

\(96.59\% \pm 1.24\%\)

\(95.93\% \pm 1.95\%\)

\(96.37\% \pm 1.14\%\)

\(94.98\% \pm 0.73\%\)

\(95.71\% \pm 1.25\%\)

\(95.13 \% \pm 1.37\%\)

AUC

\(0.9889 \pm 0.0111\)

\(0.9910 \pm 0.0070\)

\(0.9889 \pm 0.0103\)

\(0.9838 \pm 0.0131\)

\(0.9877 \pm 0.0104\)

\(0.9841 \pm 0.0104\)

Sen

\(0.9612 \pm 0.0126\)

\(0.9624 \pm 0.0224\)

\(0.9612 \pm 0.0179\)

\(0.9406 \pm 0.0194\)

\(0.9563 \pm 0.0155\)

\(0.9503 \pm 0.0201\)

Prec

\(0.9816 \pm 0.0140\)

\(0.9696 \pm 0.0175\)

\(0.9780 \pm 0.0135\)

\(0.9752 \pm 0.0139\)

\(0.9721 \pm 0.0185\)

\(0.9682 \pm 0.0172\)

Fusion #2

ACC%

\(97.09\% \pm 1.49\%\)

\(94.98\% \pm 1.56\%\)

\(96.37\% \pm 1.45\%\)

\(95.86\% \pm 2.11\%\)

\(96.73\% \pm 1.33\%\)

\(95.28 \% \pm 1.54\%\)

AUC

\(0.9910 \pm 0.0099\)

\(0.9829 \pm 0.0111\)

\(0.9908 \pm 0.0092\)

\(0.9884 \pm 0.0113\)

\(0.9892 \pm 0.0097\)

\(0.9872 \pm 0.0106\)

Sen

\(0.9660 \pm 0.0205\)

\(0.9382 \pm 0.0211\)

\(0.9600 \pm 0.0182\)

\(0.9527 \pm 0.0271\)

\(0.9685 \pm 0.0192\)

\(0.9612 \pm 0.0124\)

Prec

\(0.9853 \pm 0.0111\)

\(0.9778 \pm 0.0213\)

\(0.9791 \pm 0.0128\)

\(0.9779 \pm 0.0201\)

\(0.9771 \pm 0.0158\)

\(0.9607 \pm 0.0250\)

Fusion #3

ACC%

\(\underline{97} .\underline{46} \% \pm \underline{1} .\underline{24} \%\)

\(96.22\% \pm 1.60\%\)

-

-

-

-

AUC

\(\underline{0} .\underline{9925} \pm \underline{0} .\underline{0093}\)

\(0.9920 \pm 0.0047\)

-

-

-

-

Sen

\(\underline{0} .\underline{9769} \pm \underline{0} .\underline{0156}\)

\(0.9551 \pm 0.0229\)

-

-

-

-

Prec

\(\underline{0} .\underline{9809} \pm \underline{0} .\underline{0165}\)

\(0.9814 \pm 0.0103\)

-

-

-

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  1. The results shown in italic are of experiments that are not explored in the original research papers
  2. The underlined classification results represent the highest results for each corresponding section