Classification on Liver ultrasound dataset (10% train, 90% test) benchmark leaderboard · SOTA2 Research

Classification on Liver ultrasound dataset (10% train, 90% test)

78.5AccuracyARMA-C3

Evaluation Results

Method	Links
ARMA-C32026.05		78.5	81.8	86.6	84.1
ARMA2026.05		77.1	80.6	85.7	84
ARMA2026.05		74.5	82.4	80.1	81.1
SVC2026.05		74.2	76.7	89.7	82.6
ARMA2026.05		74.2	79.1	85.1	81.8
APPNP2026.05		73.7	78.4	85.2	81.6
ARMA2026.05		73.7	79.2	83.7	81.3
Random Forest2026.05		73.5	75.1	92.1	82.7
ARMA2026.05		73.4	78.9	83.7	81.1
GAT2026.05		73.4	81.6	79.4	80.3
MLPClassifier2026.05		73	81.7	78.2	79.9
ARMA2026.05		72.9	84.1	74.9	79
JacobiConv2026.05		72.6	79.6	81.3	80
GAT2026.05		72.6	80.7	79	79.6
XGBoost2026.05		71.5	76	85.5	80.4
GAT2026.05		71.2	84.7	69.3	76
GAT2026.05		71	80.8	76.1	78.1
GAT2026.05		70.8	82	70.4	75.2
GAT2026.05		70.4	78.3	78.8	78.4
GAT2026.05		70.2	78.1	76.7	77.3
ARMA-C32026.05		0.785	0.818	0.866	0.841
ARMA2026.05		0.771	0.806	0.857	0.84
ARMA2026.05		0.745	0.824	0.801	0.811
SVC2026.05		0.742	0.767	0.897	0.826
ARMA2026.05		0.742	0.791	0.851	0.818
APPNP2026.05		0.737	0.784	0.852	0.816
ARMA2026.05		0.737	0.792	0.837	0.813
Random Forest2026.05		0.735	0.751	0.921	0.827
ARMA2026.05		0.734	0.789	0.837	0.811
GAT2026.05		0.734	0.816	0.794	0.803
MLPClassifier2026.05		0.73	0.817	0.782	0.799
ARMA2026.05		0.729	0.841	0.749	0.79
JacobiConv2026.05		0.726	0.796	0.813	0.8
GAT2026.05		0.726	0.807	0.79	0.796
XGBoost2026.05		0.715	0.76	0.855	0.804
GAT2026.05		0.712	0.847	0.693	0.76
GAT2026.05		0.71	0.808	0.761	0.781
GAT2026.05		0.708	0.82	0.704	0.752
GAT2026.05		0.704	0.783	0.788	0.784
GAT2026.05		0.702	0.781	0.767	0.773