Fig 1.
Representative images of detected lesions for conditions of (A) normal endometrium; (B) endometrial polyp; (C) myoma; (D) AEH, and (E) endometrial cancer.
Table 1.
Images extracted from hysteroscopy videos per each disease category.
Fig 2.
Overall architecture of the model developed in this project.
Fig 3.
(A) Schematic of the training method: The training data pertaining to the malignant class were separated into two sets, Set X and Set Y. (B) Schematic of the evaluation method: image by image. (C) Schematic of the evaluation method: video unit. During image-by-image evaluation, 100 images that clearly included the lesion site were extracted from the hysteroscopic video of each patient diagnosed with a malignant tumor (Continuity analysis).
Fig 4.
(A) Trend depicting accuracy displacement of malignant and benign diagnoses in accordance with threshold value for continuity analysis. (B) Comparison between learning times required by the three neural networks. The physical time depends on the computer specifications and image size; however, the ratio of the learning time required by each network is independent of such conditions.(C) Average accuracy values obtained via image-by-image-based predictions grouped in terms of dataset and network type. (D) Average accuracy values obtained via video-unit-based predictions grouped in terms of dataset and network type.
Fig 5.
Average diagnostic accuracies for different conditions obtained using combination of 72 trained deep neural network models.
Table 2.
Diagnosis results obtained using combination of 72 trained deep neural network models.