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Table 1 Classification performance on Kather 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%

\(\underline{82} .\underline{34} \% \pm \underline{1} .\underline{87} \%\)

\(79.42\% \pm 1.27\%\)

\(78.46\% \pm 2.11\%\)

\(75. 22\% \pm 2.17\%\)

\(71.10\% \pm 1.65\%\)

\(68.28\% \pm 1.09\%\)

AUC

\(\underline{0} .\underline{9658} \pm \underline{0} .\underline{0038}\)

\(0.9646 \pm 0.0038\)

\(0.9545 \pm 0.0049\)

\(0.9552 \pm 0.0066\)

\(0.9283 \pm 0.0092\)

\(0.9340 \pm 0.0036\)

Sen

\(\underline{0} .\underline{8234} \pm \underline{0} .\underline{0186}\)

\(0.7943 \pm 0.0127\)

\(0.7846 \pm 0.0212\)

\(0.7522 \pm 0.0218\)

\(0.7111 \pm 0.0161\)

\(0.6828 \pm 0.0107\)

Prec

\(\underline{0} .\underline{8263} \pm \underline{0} .\underline{0191}\)

\(0.7961 \pm 0.0131\)

\(0.7858 \pm 0.0218\)

\(0.7568 \pm 0.0213\)

\(0.7102 \pm 0.0180\)

\(0.6826 \pm 0.0131\)

Meshkini and Ghassemian

ACC%

\(\textit{80.06}\% \pm \textit{1.54}\%\)

\(\textit{83.06}\% \pm \textit{1.33}\%\)

\(\textit{37.72}\% \pm \textit{1.78}\%\)

\(\textit{77.84}\% \pm \textit{1.17}\%\)

\(83.38\% \pm 1.81\%\)

\(83.74\% \pm 1.79\%\)

AUC

\(\textit{0.9648} \pm \textit{0.0055}\)

\(\textit{0.9800} \pm \textit{0.0081}\)

\(\textit{0.7553} \pm \textit{0.0115}\)

\(\textit{0.9649} \pm \textit{0.0028}\)

\(0.9738 \pm 0.0036\)

\(0.9816 \pm 0.0027\)

Sen

\(\textit{0.8006} \pm \textit{0.0154}\)

\(\textit{0.8307} \pm \textit{0.0134}\)

\(\textit{0.3772} \pm \textit{0.0179}\)

\(\textit{0.7784} \pm \textit{0.0117}\)

\(0.8338 \pm 0.0181\)

\(0.8374 \pm 0.0179\)

Prec

\(\textit{0.8111} \pm \textit{0.0128}\)

\(\textit{0.8325} \pm \textit{0.0124}\)

\(\textit{0.4338} \pm \textit{0.0193}\)

\(\textit{0.7792} \pm \textit{0.0105}\)

\(0.8385 \pm 0.0170\)

\(0.8400 \pm 0.0168\)

Zhou et al.

ACC%

\(\textit{64.26}\% \pm \textit{2.20}\%\)

\(\textit{67.78}\% \pm \textit{1.38}\%\)

\(\textit{51.34}\% \pm \textit{2.35}\%\)

\(\textit{54.68}\% \pm \textit{1.79}\%\)

\(60.06\% \pm 1.43\%\)

\(64.42\% \pm 1.82\%\)

AUC

\(\textit{0.9016} \pm \textit{0.0074}\)

\(\textit{0.9337} \pm \textit{0.0051}\)

\(\textit{0.8519} \pm \textit{0.0104}\)

\(\textit{0.8846} \pm \textit{0.0045}\)

\(0.8764 \pm 0.0050\)

\(0.9132 \pm 0.0061\)

Sen

\(\textit{0.6425} \pm \textit{0.0222}\)

\(\textit{0.6778} \pm \textit{0.0138}\)

\(\textit{0.5134} \pm \textit{0.0233}\)

\(\textit{0.5468} \pm \textit{0.0177}\)

\(0.6006 \pm 0.0143\)

\(0.6442 \pm 0.0183\)

Prec

\(\textit{0.6494} \pm \textit{0.0240}\)

\(\textit{0.6842} \pm \textit{0.0208}\)

\(\textit{0.5112} \pm \textit{0.0235}\)

\(\textit{0.5540} \pm \textit{0.0210}\)

\(0.6182 \pm 0.0198\)

\(0.6511 \pm 0.0197\)

Dong et al.

ACC%

\(\textit{81.10}\% \pm \textit{1.90}\%\)

\(\textit{77.50}\% \pm \textit{1.17}\%\)

\(\textit{70.66}\% \pm \textit{1.81}\%\)

\(\textit{72.10}\% \pm \textit{0.97}\%\)

\(76.62\% \pm 1.86\%\)

\(72.08\% \pm 1.65\%\)

AUC

\(\textit{0.9621} \pm \textit{0.0046}\)

\(\textit{0.9620} \pm \textit{0.0033}\)

\(\textit{0.9297} \pm \textit{0.0082}\)

\(\textit{0.9457} \pm \textit{0.0037}\)

\(0.9504 \pm 0.0063\)

\(0.9448 \pm 0.0046\)

Sen

\(\textit{0.8110} \pm \textit{0.0188}\)

\(\textit{0.7751} \pm \textit{0.0116}\)

\(\textit{0.7066} \pm \textit{0.0179}\)

\(\textit{0.7210} \pm \textit{0.0100}\)

\(0.7662 \pm 0.0185\)

\(0.7207 \pm 0.0166\)

Prec

\(\textit{0.8146} \pm \textit{0.0187}\)

\(\textit{0.7753} \pm \textit{0.0140}\)

\(\textit{0.7114} \pm \textit{0.0187}\)

\(\textit{0.7263} \pm \textit{0.0106}\)

\(0.7681 \pm 0.0190\)

\(0.7210 \pm 0.0172\)

(2): Proposed Shearlet-based methods for textured bio-medical image classification

CM

ACC%

\(86.24\% \pm 1.27\%\)

\(80.98\% \pm 1.54\%\)

\(82.82\% \pm 1.47\%\)

\(78.48\% \pm 1.30\%\)

\(80.28\% \pm 1.64\%\)

\(76.30\% \pm 1.16\%\)

AUC

\(0.9761 \pm 0.0027\)

\(0.9712 \pm 0.0031\)

\(0.9697 \pm 0.0035\)

\(0.9636 \pm 0.0046\)

\(0.9603 \pm 0.0039\)

\(0.9568 \pm 0.0051\)

Sen

\(0.8624 \pm 0.0128\)

\(0.8098 \pm 0.0155\)

\(0.8282 \pm 0.0148\)

\(0.7849 \pm 0.01274\)

\(0.8028 \pm 0.0166\)

\(0.7630 \pm 0.0114\)

Prec

\(0.8649 \pm 0.0130\)

\(0.8127 \pm 0.0141\)

\(0.8322 \pm 0.0147\)

\(0.7884 \pm 0.0115\)

\(0.8037 \pm 0.0157\)

\(0.7642 \pm 0.0127\)

LBP

ACC%

\(84.24\% \pm 0.91\%\)

\(77.90\% \pm 1.75\%\)

\(81.36\% \pm 1.16\%\)

\(75.10\% \pm 2.20\%\)

\(79.36\% \pm 1.39\%\)

\(75.38\% \pm 1.71\%\)

AUC

\(0.9724 \pm 0.0026\)

\(0.9630 \pm 0.0043\)

\(0.9649 \pm 0.0039\)

\(0.9559 \pm 0.0053\)

\(0.9590 \pm 0.0037\)

\(0.9541 \pm 0.0064\)

Sen

\(0.8424 \pm 0.0092\)

\(0.7791 \pm 0.0174\)

\(0.8136 \pm 0.0115\)

\(0.7511 \pm 0.0216\)

\(0.7936 \pm 0.0140\)

\(0.7537 \pm 0.0172\)

Prec

\(0.8454 \pm 0.0106\)

\(0.7829 \pm 0.0185\)

\(0.8171 \pm 0.0129\)

\(0.7551 \pm 0.0231\)

\(0.7964 \pm 0.0131\)

\(0.7573 \pm 0.0164\)

LOSIB

ACC%

\(\underline{86} .\underline{32} \% \pm \underline{1} .\underline{36} \%\)

\(79.78\% \pm 2.11\%\)

\(82.36\% \pm 1.82\%\)

\(79.76\% \pm 1.35\%\)

\(76.52\% \pm 1.69\%\)

\(71.24\% \pm 1.01\%\)

AUC

\(\underline{0} .\underline{9773} \pm \underline{0} .\underline{0021}\)

\(0.9703 \pm 0.0046\)

\(0.9671 \pm 0.0045\)

\(0.9689 \pm 0.0024\)

\(0.9495 \pm 0.0048\)

\(0.9402 \pm 0.0037\)

Sen

\(\underline{0} .\underline{8632} \pm \underline{0} .\underline{0134}\)

\(0.7978 \pm 0.0209\)

\(0.8236 \pm 0.0180\)

\(0.7976 \pm 0.0136\)

\(0.7653 \pm 0.0169\)

\(0.7124 \pm 0.0098\)

Prec

\(\underline{0} .\underline{8664} \pm \underline{0} .\underline{0128}\)

\(0.8017 \pm 0.0216\)

\(0.8274 \pm 0.0169\)

\(0.8008 \pm 0.0127\)

\(0.7672 \pm 0.0191\)

\(0.7128 \pm 0.0123\)

SFTA

ACC%

\(82.92\% \pm 1.41\%\)

\(78.24\% \pm 1.21\%\)

\(78.10\% \pm 1.43\%\)

\(75.04\% \pm 1.44\%\)

\(79.72\% \pm 1.07\%\)

\(75.42\% \pm 2.23\%\)

AUC

\(0.9682 \pm 0.0030\)

\(0.9645 \pm 0.0035\)

\(0.9554 \pm 0.0031\)

\(0.9570 \pm 0.0047\)

\(0.9597 \pm 0.0032\)

\(0.9574 \pm 0.0054\)

Sen

\(0.8292 \pm 0.0143\)

\(0.7825 \pm 0.0124\)

\(0.7810 \pm 0.0141\)

\(0.7504 \pm 0.0144\)

\(0.7972 \pm 0.0110\)

\(0.7543 \pm 0.0224\)

Prec

\(0.8331 \pm 0.0139\)

\(0.7845 \pm 0.0117\)

\(0.7877 \pm 0.0144\)

\(0.7539 \pm 0.0133\)

\(0.7986 \pm 0.0109\)

\(0.7587 \pm 0.0248\)

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

Fusion #1

ACC%

\(87.54\% \pm 1.85\%\)

\(81.40\% \pm 1.21\%\)

\(85.32\% \pm 1.72\%\)

\(80.30\% \pm 1.52\%\)

\(83.92\% \pm 1.84\%\)

\(77.76\% \pm 1.21\%\)

AUC

\(0.9823 \pm 0.0042\)

\(0.9734 \pm 0.0019\)

\(0.9774 \pm 0.0043\)

\(0.9707 \pm 0.0024\)

\(0.9740 \pm 0.0049\)

\(0.9619 \pm 0.0031\)

Sen

\(0.8755 \pm 0.0186\)

\(0.8141 \pm 0.0122\)

\(0.8533 \pm 0.0173\)

\(0.8029 \pm 0.0155\)

\(0.8392 \pm 0.0185\)

\(0.7776 \pm 0.0121\)

Prec

\(0.8764 \pm 0.0189\)

\(0.8154 \pm 0.0135\)

\(0.8539 \pm 0.0179\)

\(0.8078 \pm 0.0153\)

\(0.8397 \pm 0.0178\)

\(0.7798 \pm 0.0127\)

Fusion #2

ACC%

\(88.46\% \pm 1.14\%\)

\(82.28\% \pm 1.46\%\)

\(85.14\% \pm 1.05\%\)

\(80.26\% \pm 2.11\%\)

\(83.54\% \pm 1.30\%\)

\(77.20\% \pm 1.92\%\)

AUC

\(0.9804 \pm 0.0023\)

\(0.9746 \pm 0.0034\)

\(0.9749 \pm 0.0029\)

\(0.9699 \pm 0.0049\)

\(0.9693 \pm 0.0033\)

\(0.9606 \pm 0.0040\)

Sen

\(0.8846 \pm 0.0113\)

\(0.8228 \pm 0.0148\)

\(0.8514 \pm 0.0105\)

\(0.8026 \pm 0.0211\)

\(0.8354 \pm 0.0133\)

\(0.7720 \pm 0.0195\)

Prec

\(0.8874 \pm 0.0108\)

\(0.8253 \pm 0.0149\)

\(0.8551 \pm 0.0107\)

\(0.8070 \pm 0.0198\)

\(0.8375 \pm 0.0130\)

\(0.7744 \pm 0.0182\)

Fusion #3

ACC%

\(\underline{92} .\underline{54} \% \pm \underline{1} .\underline{32} \%\)

\(88.60\% \pm 2.03\%\)

-

-

-

-

AUC

\(\underline{0} .\underline{9906} \pm \underline{0} .\underline{0019}\)

\(0.9900 \pm 0.0020\)

-

-

-

-

Sen

\(\underline{0} .\underline{9254} \pm \underline{0} .\underline{0132}\)

\(0.8860 \pm 0.0203\)

-

-

-

-

Prec

\(\underline{0} .\underline{9267} \pm \underline{0} .\underline{0129}\)

\(0.8881 \pm 0.0194\)

-

-

-

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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