## 4 Experiment Results
### Quantitative Results Summary  
We applied the IMNS method to each of the datasets described in Section 2. In particular, we consider the detection of malaria, DR, and TB. The results for these three experiments are, respectively, summarized in Tables 3-5. Table 3 shows the performance metrics for the IMNS method with various IMNets for malaria detection using blood smear slide images. Note that here IMNet \(A\)-\(E\) had a significantly higher BAC (\(97.0\pm 0.36\)) than AlexNet, ResNet-50, DenseNet-201, and NasNet (\(96.2\pm 0.22\) [\(p<0.05\)], \(96.5\pm 0.51\) [\(p<0.05\)], \(96.2\pm 0.43\) [\(p<0.05\)], and \(96.7\pm 0.12\) [\(p<0.05\)], respectively). In addition, our proposed IMNet \(A\)-\(E\) outperforms the Inception v3 in this experiment (\(96.8\pm 0.39\) [\(p<0.05\)]). Furthermore, note that IMNet \(A\)-\(D\) took only 11.71 seconds to process 5512 samples (\(9\times\) faster than Inception v3). The highest AUC in this experiment is achieved with IMNet \(A\)-\(D\). Note also that in this application the addition of SubNet \(E\) lowers all of the metrics. This may suggest that the IMNS process can be halted as further improvement is not expected with additional modules. The results summary for DR detection in retinal images are shown in Table 4. Here the IMNet \(A\)-\(E\) achieved a higher BACC (\(97.92\pm 0.23\)) which is significantly better than AlexNet and NasNet (\(97.20\pm 0.52\) [\(p<0.05\)], and \(97.05\pm 0.53\) [\(p<0.05\)]). The BACC of IMNet \(A\)-\(E\) is competitive with ResNet-50, DenseNet-201 and Inception v3 (\(97.9\pm 0.73\) [\(p=0.86\)], \(98.0\pm 0.39\) [\(p=0.42\)], and \(97.8\pm 0.41\) [\(p=0.66\)], respectively). The DenseNet-201 gives the best BACC here and the ResNet-50 model does have a slightly higher AUC than IMNet \(A\)-\(E\). However, IMNet \(A\)-\(E\) processes 732 images in 7.63 s, as compared with 41.71 seconds for ResNet-50. As can be seen by the different IMNet results in Table 4, the BACC score rises with the addition of each SubNet during the IMNS process in this experiment. The results summary for TB detection in chest radiographs is presented in Table 5. The highest BACC of \(88.6\pm 2.25\) is achieved with IMNet \(A\)-\(E\), which is significantly higher than AlexNet, Inception v3, and NasNet (\(86.1\pm 2.91\) [\(p\)\(<\)\(0.05\)], \(85.5\pm 3.18\) [\(p<0.05\)], \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **BACC (\%)** & **SPEC (\%)** & **SENS (\%)** & **AUC** & **Testing Time (s)** \\ \hline AlexNet & \(97.2\pm 0.52\) & \(96.9\pm 0.82\) & \(97.4\pm 0.76\) & \(0.994\pm 0.003\) & 48.83 \\ ResNet-50 & \(97.9\pm 0.73\) & \(97.3\pm 1.08\) & \(98.5\pm 0.94\) & \(\textbf{0.997}\pm\textbf{0.001}\) & 41.71 \\ DenseNet-201 & \(\textbf{98.0}\pm\textbf{0.39}\) & \(\textbf{98.0}\pm\textbf{0.27}\) & \(97.9\pm 0.82\) & \(0.996\pm 0.002\) & 64.49 \\ Inception v3 & \(97.8\pm 0.41\) & \(97.2\pm 0.81\) & \(\textbf{98.5}\pm\textbf{0.74}\) & \(0.995\pm 0.002\) & 43.12 \\ NasNet & \(97.0\pm 0.53\) & \(96.7\pm 0.85\) & \(97.3\pm 0.81\) & \(0.994\pm 0.001\) & 55.41 \\ IMNet \(A\) & \(92.2\pm 3.90\) & \(93.8\pm 3.26\) & \(90.6\pm 8.85\) & \(0.980\pm 0.009\) & **6.85** \\ IMNet \(A\), \(B\) & \(96.1\pm 0.80\) & \(94.8\pm 2.18\) & \(97.4\pm 1.28\) & \(0.991\pm 0.003\) & 7.25 \\ IMNet \(A\)-\(C\) & \(97.0\pm 0.50\) & \(96.6\pm 0.82\) & \(97.4\pm 0.91\) & \(0.995\pm 0.001\) & 7.49 \\ IMNet \(A\)-\(D\) & \(97.7\pm 0.39\) & \(97.7\pm 1.01\) & \(97.7\pm 0.98\) & \(0.996\pm 0.001\) & 7.51 \\ IMNet \(A\)-\(E\) & \(97.9\pm 0.23\) & \(98.0\pm 0.65\) & \(97.7\pm 0.35\) & \(0.996\pm 0.001\) & 7.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Diabetic retinopathy dataset results showing hold-out validation performance on the test set using our IMNS method and benchmark methods.