Analysis of Medical Image Resizing Using Bicubic Interpolation Algorithm

Triwijoyo, Bambang Krismono; Adil, A.

doi:10.24843/jik.2021.v14.i01.p03

Cited by 18 publications

(8 citation statements)

References 7 publications

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“…Before being used in image processing, the dataset is divided into train (70%) and test (30%) datasets. The first step in image preprocessing is image scaling, where images are resized to a specified pixel width using the bicubic interpolation method [ 75 , 76 , 77 , 78 , 79 , 80 , 81 ]. Each image in the dataset varied in size; therefore, images were scaled to a standard size to improve faster and easier processing.…”

Section: Methodsmentioning

confidence: 99%

Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

2023

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The prognosis for patients with skin cancer improves with regular screening and checkups. Unfortunately, many people with skin cancer do not receive a diagnosis until the disease has advanced beyond the point of effective therapy. Early detection is critical, and automated diagnostic technologies like dermoscopy, an imaging device that detects skin lesions early in the disease, are a driving factor. The lack of annotated data and class-imbalance datasets makes using automated diagnostic methods challenging for skin lesion classification. In recent years, deep learning models have performed well in medical diagnosis. Unfortunately, such models require a substantial amount of annotated data for training. Applying a data augmentation method based on generative adversarial networks (GANs) to classify skin lesions is a plausible solution by generating synthetic images to address the problem. This article proposes a skin lesion synthesis and classification model based on an Improved Deep Convolutional Generative Adversarial Network (DCGAN). The proposed system generates realistic images using several convolutional neural networks, making training easier. Scaling, normalization, sharpening, color transformation, and median filters enhance image details during training. The proposed model uses generator and discriminator networks, global average pooling with 2 × 2 fractional-stride, backpropagation with a constant learning rate of 0.01 instead of 0.0002, and the most effective hyperparameters for optimization to efficiently generate high-quality synthetic skin lesion images. As for the classification, the final layer of the Discriminator is labeled as a classifier for predicting the target class. This study deals with a binary classification predicting two classes—benign and malignant—in the ISIC2017 dataset: accuracy, recall, precision, and F1-score model classification performance. BAS measures classifier accuracy on imbalanced datasets. The DCGAN Classifier model demonstrated superior performance with a notable accuracy of 99.38% and 99% for recall, precision, F1 score, and BAS, outperforming the state-of-the-art deep learning models. These results show that the DCGAN Classifier can generate high-quality skin lesion images and accurately classify them, making it a promising tool for deep learning-based medical image analysis.

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Section: Methodsmentioning

confidence: 99%

Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

2023

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“…The input image's original size is scaled down to 256 × 256 pixels. The use of deep learning algorithms by CNN is made simple by the altering size of the images 21 . Image resizing can be done based on different interpolation techniques such as bicubic and bilinear.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The use of deep learning algorithms by CNN is made simple by the altering size of the images. 21 Image resizing can be done based on different interpolation techniques such as bicubic and bilinear. Difference between bilinear and bicubic interpolation is that four nearest neighbors is considered in case of bilinear and 16 nearest neighbors is taken while considering bicubic interpolation.…”

Section: Image Resizingmentioning

confidence: 99%

Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier

Jain,

Bhatnagar,

Rao

et al. 2023

Computational Intelligence

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Artificial intelligence and deep learning have aided ocular disease through experiments including automatic illness recognition from images of the iris, fundus, or retina. Automated diagnosis systems (ADSs) provide services for the benefit of humanity and are essential in the early detection of harmful diseases. In fact, early detection is essential to avoid total blindness. In real life, several diagnostic tests such as visual ocular tonometry, retinal exam, and acuity test are performed, but they are conclusively time demanding and stressful for the patient. To consume time and detect the retinal disease earlier, an efficient prediction method is designed. In this proposed model, the first process is data collection that consists of a retinal disease dataset for testing and training. The second process is pre‐processing, which executes image resizing and noise filter for feature extraction. The third step is feature extraction, which extracts the image's form, size, color, and texture for classification with CNN based on Inception‐ResNet V2. The classification process is done by using the SVM with the extracted features. The prediction of diseases is classified such as normal, cataract, glaucoma, and retinal disease. The suggested model's performance is assessed using performance indicators such as accuracy, error, sensitivity, precision, and so forth. The suggested model's accuracy, error, sensitivity, and precision are 0.96, 0.962, 0.964, and 0.04, respectively, higher than existing techniques such as VGG16, Mobilenet V1, ResNet, and AlexNet. Thus, the proposed model instantly predicts retinal disease.

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“…The scale of the detected image is normalized by the bilinear interpolation algorithm. A grayscale image of 224 × 224 pixels is output and saved [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Emotion Recognition of Online Education Learners by Convolutional Neural Networks

2022

Computational Intelligence and Neuroscience

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At present, the facial expression recognition model in video communication has problems such as weak network generalization ability and complex model structure, which leads to a large amount of computation. Firstly, the Inception architecture is adopted as a design philosophy. The Visual Geometry Group Network (VGGNet) model is improved. Multiscale kernel convolutional layers are constructed to obtain more expressive features. Secondly, the attention mechanism is integrated into a multiscale feature fusion network to form a multiattention mechanism Convolutional Neural Network (CNN) model. Novel spatial and multichannel attention models are designed. The effects of redundant information and noise are reduced. Finally, experiments are carried out on the Fer2013 dataset and the Extended Cohn-Kanade Dataset (CK+) to verify the detection accuracy of the model. The results show that the Delivered Duty Unpaid (DDU) loss can be used for facial expression recognition in complex environments. After the attention module is added, the overall recognition accuracy of the network on Fer2013 and CK+ has been improved to varying degrees. The addition of the channel attention module has a more obvious effect on the recognition accuracy compared with the spatial attention module. The addition of the attention module enables the network to increase the attention to error-prone samples. The improved network model can better extract the key features of facial expressions, enhance the feature discrimination ability, and improve the recognition accuracy of error-prone expressions. The accuracy rate of facial expression recognition with larger movements is over 98%. Facial expressions are an important way of communication between people, and online video has greatly limited this communication method. The proposed CNN model based on multiscale feature fusion will effectively solve these network limitations and have an important and positive impact on future network information exchange.

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Analysis of Medical Image Resizing Using Bicubic Interpolation Algorithm

Cited by 18 publications

References 7 publications

Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier

Emotion Recognition of Online Education Learners by Convolutional Neural Networks

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