## 4 Experiments
### Comparison with State-of-the-Art Approaches  
In this study, we compared state-of-the-art approaches using transfer learning for each application as follows: (1) cervical cancer classification, [PERSON] et al. [16], [PERSON] et al. [17], [PERSON] et al. [12], and [PERSON] et al. [14]; (2) skin lesion classification, [PERSON] et al. [48], [PERSON] et al. [49], and [PERSON] et al. [45]; and (3) pressure ulcer segmentation, [PERSON] et al. [62], [PERSON] et al. [66], [PERSON] et al. [57], [PERSON] et al. [64], and [PERSON] et al. [67]. For comparison with the state-of-the-art approaches, we followed the common metrics used in prior publications. As shown in Table 7, each application experiment's ensemble performance showed improvement over the state-of-the-art results. In the classification of cervical cancer, the ensemble model was selected from the approach with the highest accuracy, and the performance was improved by 3.5% and 18.2% in accuracy and sensitivity, respectively, compared to [12] using transfer learning. Also, the performance was improved by 19.76% and 9.35% in accuracy and sensitivity, compared to [14]. The accuracy and sensitivity of the ensemble were 4.6% and 24.1% higher, respectively, than those in [16], and the accuracy of [17] was 3.44%, which was slightly higher compared to the ensemble model. However, sensitivity is important in medical diagnosis, so the sensitivity being higher than that in the other studies was significant. In skin lesion classification, compared to the existing state-of-the-art approaches using transfer learning [48; 49], the accuracy improved by 8.7%, precision by 28.3%, and sensitivity by 39.7%. Also, when compared to [45] using transfer learning with online deep clustering and momentum contrastive learning, the accuracy improved by 10.6%. Because the HAM10000 dataset has seven skin lesion types and it is important to consider the overall performance balance, the ensemble model of skin classification consisted of the approach with the highest accuracy, \(T_{roi}\). The accuracy and sensitivity performance were also significant, and overall, the ensemble model performed better than the state-of-the-art approaches. Finally, for pressure ulcer segmentation, the accuracy and IoU were 4% and 52.6%, respectively, better than those in [62], and the accuracy was 3% higher than that in [66]. When compared to [57], the IoU and DSC of the ensemble model were 16.9% and 6.4% higher those in [57]. In addition, the DSC was 3.5% better than that in [64] using transfer learning, where only the DSC metric could be compared. The accuracy and DSC were 1.1% and 8.4% better than those in [67] using transfer learning. As shown in Figure 10a, the cervical cancer classification ensemble performance had the highest true positive and true negative values. In addition, the skin cancer classification ensemble performance obtained a balanced true positive value for each class, as shown in Figure 10b. Furthermore, the pressure ulcer mask predicted by the ensemble was similar to the ground truth, as shown in Figure 10c. Therefore, the proposed approach to overcome a lack of data in medical image analysis was effective in applications representative of medical imaging tasks. Consequently, the image and object classification task focused on the RoI to classify diseases or lesions effectively using transfer learning with Rols. Furthermore, the segmentation task of learning a global shape was accomplished by transfer learning with similar images.