Segmentation of cancer cell in histopathologic images of breast cancer and lesion area in skin cancer images using convolutional neural networks

2020 ◽  
Vol 42 (5) ◽  
pp. 520-528
Author(s):  
Saber Fooladi ◽  
Hassan Farsi ◽  
sajad Mohamadzadeh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Pathology ◽  
Diagnostic System ◽  
High Sensitivity ◽  
Skin Lesions ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Significant Difference

Background: Pathological analysis plays an important role in the diagnosis, prediction and planning of cancer treatment. Using digital pathology, ie, scanning and storing digital parts of patient tissue, tools for analyzing these complex images now can be developed. Doctors use a computer diagnostic system from an intelligent assistant to accurately diagnose. These systems have great benefits in improving treatment efficacy. Methods: In this study, the deep neural network classifier has been used with the help of the Tensor Flow framework and the use of the Keras-library. Input images are initially transmitted from a low pass filter to reduce noise effects. The pre-processed images are then imported into a convolutional neural network. Results: The results of the research reveal a significant difference in the accuracy values between different methods with the proposed method, which in some cases indicates an increase of more than 14.18% in the accuracy of the diagnosis. Another advantage of the proposed method is to provide high sensitivity to histopathologic images, which shows an increase of 12 to 18 percent compared to other studies. The reason for this is the excellence of extracting high-level features through convolutional neural network, which is accompanied by a reduction in the size of the feature vector. Conclusion: The results showed a accuracy of %98.6 for skin lesions and %96.1 accuracy for breast cancer histopathologic findings, which offers promising results compared to the results of other studies.

scholarly journals Detection of Computer Graphics Using Attention-Based Dual-Branch Convolutional Neural Network from Fused Color Components

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4743
Author(s):  
Peisong He ◽  
Haoliang Li ◽  
Hongxia Wang ◽  
Ruimei Zhang
Keyword(s):  
Neural Network ◽  
Computer Graphics ◽  
Convolutional Neural Network ◽  
Video Game ◽  
Detection Methods ◽  
Post Processing ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Generalized Patterns ◽  
Color Components

With the development of 3D rendering techniques, people can create photorealistic computer graphics (CG) easily with the advanced software, which is of great benefit to the video game and film industries. On the other hand, the abuse of CGs has threatened the integrity and authenticity of digital images. In the last decade, several detection methods of CGs have been proposed successfully. However, existing methods cannot provide reliable detection results for CGs with the small patch size and post-processing operations. To overcome the above-mentioned limitation, we proposed an attention-based dual-branch convolutional neural network (AD-CNN) to extract robust representations from fused color components. In pre-processing, raw RGB components and their blurred version with Gaussian low-pass filter are stacked together in channel-wise as the input for the AD-CNN, which aims to help the network learn more generalized patterns. The proposed AD-CNN starts with a dual-branch structure where two branches work in parallel and have the identical shallow CNN architecture, except that the first convolutional layer in each branch has various kernel sizes to exploit low-level forensics traces in multi-scale. The output features from each branch are jointly optimized by the attention-based fusion module which can assign the asymmetric weights to different branches automatically. Finally, the fused feature is fed into the following fully-connected layers to obtain final detection results. Comparative and self-analysis experiments have demonstrated the better detection capability and robustness of the proposed detection compared with other state-of-the-art methods under various experimental settings, especially for image patch with the small size and post-processing operations.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

AUTOMATED SCREENING OF DIABETIC RETINOPATHY WITH OPTIMIZED DEEP CONVOLUTIONAL NEURAL NETWORK: ENHANCED MOTH FLAME MODEL

2021 ◽  
Vol 21 (01) ◽  
pp. 2150005
Author(s):  
ARUN T NAIR ◽  
K. MUTHUVEL
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Blood Vessel ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Vessel Segmentation ◽  
Deep Convolutional Neural Network ◽  
Gray Level ◽  
Pass Filter ◽  
Blood Vessel Segmentation

Nowadays, analysis on retinal image exists as one of the challenging area for study. Numerous retinal diseases could be recognized by analyzing the variations taking place in retina. However, the main disadvantage among those studies is that, they do not have higher recognition accuracy. The proposed framework includes four phases namely, (i) Blood Vessel Segmentation (ii) Feature Extraction (iii) Optimal Feature Selection and (iv) Classification. Initially, the input fundus image is subjected to blood vessel segmentation from which two binary thresholded images (one from High Pass Filter (HPF) and other from top-hat reconstruction) are acquired. These two images are differentiated and the areas that are common to both are said to be the major vessels and the left over regions are fused to form vessel sub-image. These vessel sub-images are classified with Gaussian Mixture Model (GMM) classifier and the resultant is summed up with the major vessels to form the segmented blood vessels. The segmented images are subjected to feature extraction process, where the features like proposed Local Binary Pattern (LBP), Gray-Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) are extracted. As the curse of dimensionality seems to be the greatest issue, it is important to select the appropriate features from the extracted one for classification. In this paper, a new improved optimization algorithm Moth Flame with New Distance Formulation (MF-NDF) is introduced for selecting the optimal features. Finally, the selected optimal features are subjected to Deep Convolutional Neural Network (DCNN) model for classification. Further, in order to make the precise diagnosis, the weights of DCNN are optimally tuned by the same optimization algorithm. The performance of the proposed algorithm will be compared against the conventional algorithms in terms of positive and negative measures.

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