Ophthalmic diagnosis using deep learning with fundus images - A critical review.

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.

/pdf/diagnosis-of-cervical-cancer-based-on-ensemble-deep-learning-245qdi27s2.pdf

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images.

UGIF-Net: An Efficient Fully Guided Information Flow Network for Underwater Image Enhancement

Background: Underwater images, in general, suffer from low contrast and high color distortions due to the non-uniform attenuation of the light as it propagates through the water. In addition, the degree of attenuation varies with the wavelength, resulting in the asymmetric traversing of colors. Despite the prolific works for underwater image restoration (UIR) using deep learning, the above asymmetricity has not been addressed in the respective network engineering. Contributions: As the first novelty, this article shows that attributing the right receptive field size ( context ) based on the traversing range of the color channel may lead to a substantial performance gain for the task of UIR. Further, it is important to suppress the irrelevant multi-contextual features and increase the representational power of the model. Therefore, as a second novelty, we have incorporated an attentive skip mechanism to adaptively refine the learned multi-contextual features. The proposed framework, called Deep WaveNet , is optimized using the traditional pixel-wise and feature-based cost functions. An extensive set of experiments have been carried out to show the efficacy of the proposed scheme over existing best-published literature on benchmark datasets. More importantly, we have demonstrated a comprehensive validation of enhanced images across various high-level vision tasks, e.g., underwater image semantic segmentation and diver’s 2D pose estimation. A sample video to exhibit our real-world performance is available at https://tinyurl.com/yzcrup9n . Also, we have open-sourced our framework at https://github.com/pksvision/Deep-WaveNet-Underwater-Image-Restoration . 

/pdf/wavelength-based-attributed-deep-neural-network-for-qyxgpxol.pdf

Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration

Cervical cancer is one among the trivial forms of cancer that counts for 6.6% of all females cancers with an estimated 570,000 new cases in 2018.
The mortality rate due to cervical cancer is approximately 90% in low or middle income countries due to lack of suitable pre-screening procedures and experienced medical staff. Colposcopy images or cervigrams, are the images that capture the cervical region, are considered as the gold standard by the medical experts for the identification and evaluation of cervical cancer. The visual assessment of cervigrams for recognizing cancer suffers from high inter- or intra-variations especially among less or unskilled medical experts. However, this method is dependent on colposcopists’ observation and it is more time consuming, tedious and laborious task which calls for development of computer-aided method for diagnosis of cervical cancer. With the technological advancements, deep learning has been commonly employed for providing automated solutions for disease diagnosis due to its self-learning capability. This paper presents a deep-learning-based method for cervix cancer classification using colposcopy images. The architecture of the proposed method namely, ColpoNet, has been motivated by the DenseNet model because it is computationally more efficient as compared to other models. Further, the method has been tested and validated on the dataset released by the National Cancer Institute and it has been compared with other deep-learning models namely AlexNet, VGG16, ResNet50, LeNet and GoogleNet to check scope of its applicability. The experimental analysis revealed that ColpoNet achieved an accuracy of 81.353% and shows the highest performance rate as compared to other state-of-the-art deep techniques. Such classification system can be deployed in clinics to enhance the early detection of cervical cancer in less developed countries.

ColpoNet for automated cervical cancer screening using colposcopy images

Cervical precancerous lesions classification using pre-trained densely connected convolutional networks with colposcopy images

Detection of cervical lesion region from colposcopic images based on feature reselection

Underwater images suffer from serious color distortion and detail loss because of the wavelength-dependent light absorption and scattering, which seriously influences the subsequent underwater object detection and recognition. The latest methods for underwater image enhancement are based on deep models, which focus on finding a mapping function from the underwater image subspace to a ground-truth image subspace. They neglect the diversity of underwater conditions which leads to different background colors of underwater images. In this paper, we propose a Class-condition Attention Generative Adversarial Network (CA-GAN) to enhance an underwater image. We build an underwater image dataset which contains ten categories generated by the simulator with different water attenuation coefficient and depth. Relying on the underwater image classes, CA-GAN creates a many-to-one mapping function for an underwater image. Moreover, in order to generate the realistic image, attention mechanism is utilized. In the channel attention block, the feature maps in the front-end layers and the back-end layers are fused along channels, and in the spatial attention block, feature maps are pixel-wise fused. Extensive experiments are conducted on synthetic and real underwater images. The experimental results demonstrate that CA-GAN can effectively recover color and detail of various scenes of underwater images and is superior to the state-of-the-art methods.

/pdf/ca-gan-class-condition-attention-gan-for-underwater-image-2dbhxqx0rf.pdf

CA-GAN: Class-Condition Attention GAN for Underwater Image Enhancement

Retinal Vessel Segmentation Using Densely Connected Convolution Neural Network with Colorful Fundus Images

Cervical cancer is the most common genital malignant tumor which seriously threats to women's health, especially in poor areas. As one of the screening methods for cervical lesions, colposcopy plays an important role in the early stage of cervical lesions. However, due to the physiological mucus in the human body, the specular reflection region (SR region) is often shown in a high-definition image obtained by doctors through a colposcopy. The expression of the highlight area in the colposcopic image has a certain similarity with the abnormal metaplasia epithelium (AW area, i.e. the lesion area), which leads to a series of problems in the medical image processing in the later stage, such as the segmentation of the lesion area, misdiagnosis by doctors, etc. Therefore, this paper proposed the exemplar-based inpainting method combining with the improved threshold method to detect the SR regions. In the experiment, 150 sets of data (50 pieces for different degrees of cervical lesions) were inpainted by this paper and the other two existing inpainting methods, and the results were compared with subjective and objective quality. The experiment showed that the exemplar-based method has better effect in removing the specular reflection in cervical images.

Detection and Inpainting of Specular Reflection in Colposcopic Images with Exemplar-based Method

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