EczemaNet: A Deep CNN-based Eczema Diseases Classification

Junayed, Masum Shah; Sakib, Abu Noman Md; Anjum, Nipa; Islam, Md Baharul; Jeny, Afsana Ahsan

doi:10.1109/ipas50080.2020.9334929

Cited by 26 publications

(15 citation statements)

References 17 publications

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“…It has gained popularity as a technique for deciphering medical images [19,20,31,32] as a result of the emergence of new network variants and the introduction of reliable parallel solvers tailored for modern GPUs. It has been used successfully in various medical image processing, analysis, and classification applications by researchers [16,[33][34][35][36]. Local textural elements containing complex, high-level structures with a particular orientation are used to categorize ILD patterns in CT images instead of random artifacts.…”

Section: Proposed Architecturementioning

confidence: 99%

“…To see how the amount of convolution blocks and optimizers (ADAM and MSE) impacts the accuracy of the classification and, as a result, the ILD recognition, three different models based fine-tuning on 3, 4, and 5 blocks with three different sets of filters, i.e. (16,32,64), (32,64,32,128), and (32,64,32,64,128), respectively, are developed and evaluated on the dataset. Table 2 represents the accuracy achieved from these models.…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

“…However, these works achieved less accuracy and computationally expensive due to inappropriate feature selection. Recently, Deep Learning (DL) techniques have already shown superior performance in various computer vision issues, raising hopes in the area, including medical image processing [15], classification [16], segmentation [17,18], and detection [19,20]. Moreover, specific DL approaches called the pre-trained model of convolutional neural network (CNN), such as AlexNet [21], VGG-16 [22], ResNet-50 [23], were used to achieve competitive accuracy.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification

2021

Turk J Elec Eng & Comp Sci

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The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model. A comparison study showed that the presented model outperformed pre-trained CNNs and five-fold cross-validation on the same dataset. For ILDs pattern classification, the proposed approach achieved the accuracy scores of 99.09% and the average F score of 97.9% that outperforms three pre-trained CNNs. These outcomes show that the proposed model is relatively state-of-the-art in precision, recall, f score, and accuracy.

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Section: Proposed Architecturementioning

confidence: 99%

Section: Hyperparameter Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification

2021

Turk J Elec Eng & Comp Sci

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“…The recent development of machine learning (ML) methods, together with the need for telemedicine, resulted in an increasing interest in developing computer vision algorithms for automatic evaluation of AD severity from digital images [2] [3] [4] [5]. Those algorithms generally consist of two steps: (i) identifying areas covered by eczema in each image - so that the images are “segmented” - either manually as part of the data pre-processing (human-in-the-loop) or automatically by an algorithm, and then (ii) predicting the severity of eczema features in the segmented areas.…”

Section: Introductionmentioning

confidence: 99%

Detecting eczema areas in digital images: an impossible task?

Hurault

Pan

Mokhtari

et al. 2022

Preprint

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Assessing the severity of atopic dermatitis (AD, or eczema) traditionally relies on a face-to-face assessment by healthcare professionals, and may suffer from inter- and intra-rater variability. With the expanding role of telemedicine, several machine learning algorithms have been proposed to automatically assess AD severity from digital images. Those algorithms usually detect and then delineate (“segment”) AD lesions before assessing lesional severity, and are trained using the data of AD areas detected by healthcare professionals. To evaluate the reliability of such data, we estimated the inter-rater reliability of AD segmentation in digital images.Four dermatologists independently segmented AD lesions in 80 digital images collected in a published clinical trial. We estimated the inter-rater reliability of the AD segmentation using the intra-class correlation coefficients (ICCs) at the pixel-level and the area-levels for different resolutions of the images. The average ICC was 0.45 (SE = 0.04) corresponding to a “poor” agreement between raters, while the degree of agreement for AD segmentation varied from image to image.The AD segmentation in digital images is highly rater-dependent even between dermatologists. Such limitations need to be taken into consideration when the AD segmentation data are used to train machine learning algorithms that assess eczema severity.

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“…III. PROPOSED CATARACTNET ARHITECTUREConvolutional Neural Network (CNN) is a deep neural network that acquires a complex hierarchy of features by convolutional, and pooling layers and non-linear activation functions[46],[47]. The feature extraction phase and the classification process are integrated into deep learning-based methods while these two steps are separated in the manual feature extraction methods.…”

mentioning

confidence: 99%

CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images

Junayed

Islam²,

Sadeghzadeh

et al. 2021

IEEE Access

Self Cite

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Cataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet, is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers. Thus, the computational cost and average running time of CataractNet are significantly reduced compared to other pre-trained Convolutional Neural Network (CNN) models. The proposed network is optimized with the Adam optimizer. A total of 1130 cataract and non-cataract fundus images were collected and augmented to 4746 images to train the model. For avoiding the over-fitting problem, the dataset is extended through augmentation before model training. Experimental results prove that the proposed method outperforms the state-of-the-art cataract detection approaches with an average accuracy of 99.13%.

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EczemaNet: A Deep CNN-based Eczema Diseases Classification

Cited by 26 publications

References 17 publications

An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification

An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification

Detecting eczema areas in digital images: an impossible task?

CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images

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