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4 days ago · Li and Momen Citation (2021) assessed 4 state-of-the-art CNN models, namely Inception V3, ResNet50, VGG16, and VGG19, to classify optical satellite images into 5 different weather events. It was identified that Inception V3 was the best-performing model with an average accuracy of 92%.
2 days ago · Inception-V3: Inception-V3 (also known as Google Net) is one of the most popular inventions by Google , in the domain of CNN. Inception model is inspired by the concept of multi-scale (or, multi-resolution) feature extraction. In many images, the objects (or, foregrounds) may not always appear on the same scale.
4 days ago · In this framework, the Inception-v3 architecture used as an encoder that empowers network to capture better high-level and abstract tumor specific features . The Inception-v3 architecture that are stacked together by multiple enhanced inception and reduction blocks as explained in Section 3.1.
2 days ago · The image dataset consisted of the three mentioned disease classes and one healthy class. Additional images were generated through rotation and brightness enhancement methods to address the class imbalance. Three different architectures based on the Inception-v3 network framework were trained using a baseline training approach.
4 days ago · To address this gap, we introduce RetNet30, a novel stacked convolutional neural network (CNN) designed to revolutionize automated retinal disease diagnosis. RetNet30 combines a custom-built 30-layer CNN with a fine-tuned Inception V3 model, integrating these sub-models through logistic regression to achieve superior classification performance.
Inception v3 Design Philosophy. Inception v3 is known for its “Inception modules,” which contain parallel convolutions with different kernel sizes. This architecture effectively captures features at various scales. Strengths. Exceptional accuracy; Robust to variations in input size; Efficient use of parameters
Sep 20, 2024 · Inception V3. Research suggests that the Inception V3 model may be effectively utilized for the identification and categorization of novel images by modifying the fully connected layer design and preserving the convolution layer parameters. On the ImageNet dataset, the image recognition model Inception V3 has demonstrated accuracy levels above ...
