Medical Video Processing

Goswami, Srijan; Dey, Urmimala; Roy, Payel; Ashour, Amira S.; Dey, Nilanjan

doi:10.4018/978-1-5225-1025-3.ch001

Cited by 3 publications

(1 citation statement)

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“…Medical Imaging is a technique of visualizing body parts to conduct clinical analysis or get a medical response. Furthermore, it also builds a database of body anatomy and physiology, allowing experts to identify abnormalities [4,6]. Briefly, the medical imaging is started from the sensor's stage which penetrates the human body, subsequently it is transformed into signals and read by the detector, continuously mathematically manipulated and eventually visualized into an image [9].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the efficiency of an interconnected convolutional neural network by classifying medical images

Lantang¹,

Terdik²,

Hajdú³

et al. 2021

AMI

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Convolutional Neural Network (CNN) for medical image classification has produced satisfying work [11,12,15]. Several pretrained models such as VGG19 [17], InceptionV3 [18], and MobileNet [8] are architectures that can be relied on to design high accuracy classification models. This work investigates the performance of three pretrained models with two methods of training. The first method trains the model independently, meaning that each model is given an input and trained separately, then the best results are determined by majority voting. In the second method the three pretrained models are trained simultaneously as interconnected models.The interconnected model adopts an ensemble architecture as is shown in [7]. By training multiple CNNs, this work gives optimum results compared to a single CNN. The difference is that the three subnetworks are trained simultaneously in an interconnected network and showing one expected result.

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Section: Introductionmentioning

confidence: 99%