We roughly divided those clinicians into specialists and non-specialists according to their working experience and the number of times of performing endoscopy yearly. The involving ML models were as follows: VGG-16, ResNet50, VGG-19, SVM, PLS-DA, ResNet34, DeepLabv3, GoogLeNet, EfficientDet, ...
Singh and Misra [34] detailed how the soft computing methods and segmentation of images aid in plant, pest, and disease identification and classification in mostly grown plants like Malus domestica (apple), Zea mays, and genus Vitis diseases using pre-trained CNNs like VGG16 model, some other...
The proposed model in this study is developed to overcome these limitations. The mutation information of 40 genes that cause thyroid cancer is derived fromhttps://intogen.org/[27] and the normal gene sequences are downloaded fromhttps://asia.ensembl.org/[28] with web scraping code written in...
We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively.Peer Review reports Introduction Segmenting skin lesions using computer assistance becomes difficult ...
learner. Also, two different hybrid approaches are used as benchmark classifiers. Qaid et al. [23] uses a pre-trained Visual Geometry Group (VGG) model as the feature learner and an XGBoost as a classifier (VGG + XGBoost), and we use another hybrid model that uses a VGG for data...
Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of
(Within Sim. /WS and Across Sim. /AS) of images as two high-level image statistics. In this work, VGG-16 (Simonyan & Zisserman,2014) was used to extract image features. As mentioned earlier, we conducted an eye movement experiment using a subset of 240 images from the AID dataset. ...
The diagnostic performance of the VGGNet-16 model is slightly better than that of the three gynecologists in both classification tasks. With the aid of the model, the overall accuracy of the diagnosis of endometrial lesions by gynecologists can be improved. Conclusions The VGGNet-16 model ...
A class activation map of each image is extracted from the trained model without mosaic dataset. c The mosaic generator randomly chooses a point within a square to form a 2 × 2 grid so that one segment should be, at least, larger than half of the synthesized image. A mosaic image...
In this study, we used VGG16 [34], AlexNet [35], ResNet18 and ResNet50 [36] for training. All these networks were pre-trained on the ImageNet public dataset to classify the images into 1000 classes. These networks differed in the input size, number of layers, and the number of the...