A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images

• The accuracy of the proposed model was calculated as high as 0.9 . • A novel 1-dimensional CNN model was proposed. • The features were automatically selected thanks to the proposed model. • The experiments were conducted on the de facto datasets. • We shed light on the insights of Android malware through the conducted experiments. Smartphones have become an integral part of our daily lives thanks to numerous reasons. While benefitting from what they offer, it is critical to be aware of the existence of malware in the Android ecosystem and be away from them. To this end, an end-to-end and highly effective Android malware detection framework based on CNN, namely, DroidMalwareDetector , was proposed within this study. Unlike most of the related work, DroidMalwareDetector was specifically designed to ( i ) automate feature extraction and selection, ( ii ) propose a novel CNN that operates with 1 -dimensional data, and ( iii ) use intents and API calls alongside the widely used permissions to perform comprehensive malware analysis. The proposed framework was trained and evaluated on the constructed dataset, which consisted of 14 , 386 apps from the de-facto standard datasets. The proposed framework’s efficiency in terms of distinguishing malware from benign apps was revealed thanks to the conducted experiments. According to the experimental result, the accuracy of the proposed framework was calculated as high as 0.9 , which was higher than the accuracy values obtained from a wide range of machine learning algorithms. The insights which were gained through the conducted experiments were revealed as another contribution to the research field. 

DroidMalwareDetector: A novel Android malware detection framework based on convolutional neural network

Breast cancer has high incidences and mortality rates in women worldwide. Malignancy could be detected manually by experienced pathologists based on Hematoxylin and Eosin (H&E) stained images. However, it is time‐consuming and experience‐dependent, making early diagnosis a big challenge. In this paper, a methodology for breast cancer classification based on histopathological images with deep learning was described. A residual learning‐based convolutional neural network named myResNet‐34 was designed for malignancy‐and‐benign classification. In addition, an algorithm automatically generating the target image for stain normalization was proposed, which eliminated the bias caused by manual selection of the reference image. Elastic distortion was introduced and combined with affine transformation for data augmentation considering the characteristics of the H&E images. Experiments were conducted on BreakHis dataset with the proposed framework. Promising results were achieved with an average classification accuracy of around 91% on image‐level classification. Results indicated that both our data augmentation and stain normalization effectively improved the classification accuracy by 2‐3%.

Classification of breast cancer histopathological image with deep residual learning

Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of lives and amounts of money. It is the world’s leading type of cancer among women and a leading cause of mortality and morbidity. The histopathological examination of breast tissue biopsies is the gold standard for diagnosis. In this paper, a computer-aided diagnosis (CAD) system based on deep learning is developed to ease the pathologist’s mission. For this target, five pre-trained convolutional neural network (CNN) models are analyzed and tested—Xception, DenseNet201, InceptionResNetV2, VGG19, and ResNet152—with the help of data augmentation techniques, and a new approach is introduced for transfer learning. These models are trained and tested with histopathological images obtained from the BreakHis dataset. Multiple experiments are performed to analyze the performance of these models through carrying out magnification-dependent and magnification-independent binary and eight-class classifications. The Xception model has shown promising performance through achieving the highest classification accuracies for all the experiments. It has achieved a range of classification accuracies from 93.32% to 98.99% for magnification-independent experiments and from 90.22% to 100% for magnification-dependent experiments.

https://www.mdpi.com/2306-5354/9/8/391/pdf?version=1660549657

A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer

: Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature extraction step to enhance and preserve the fine details of the breast MRI scans boundaries by using fractional integral entropy FIE algorithm, to reduce the effects of the intensity variations between MRI slices, and finally to separate the right and left breast regions by exploiting the symmetry information. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, all extracted features significantly improves the performance of the LSTM network to precisely discriminate between pathological and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 326 T2W-TSE images and 326 STIR images is 98.77%. The experimental results demonstrate that FIE enhancement method improve the performance of CNN in classifying breast MRI scans. The proposed model appears to be efficient and might represent a useful diagnostic tool in the evaluation of MRI breast scans.

https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/download/6782/4036

Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction

Breast cancer is the most common cancer worldwide with high death rate especially for women, early diagnosis can increase the survival opportunity with correct treatment in the hospital. The Computer-Assisted Diagnosis (CAD) system is of vital significant for improving the diagnostic accuracy as the diagnosis process is tedious and the result may be different between pathologists. In this paper, we proceed research on breast cancer histopathological images classification based on deep Convolutional Neural Network (CNN). The approach proposed in this work utilize CNN to extract features of histopathological images and classify the images into begin tumors and malignant tumors by softmax function. Eliminate overfitting phenomenon by data augmentation and fine tune technology while improving the performance of networks by cross validation training strategy. The experiments in this paper were conducted on BreaKHis database [4] available to scientific study from 2014 and the results proved the algorithm’s advantage on accuracy.

Breast Cancer Diagnosis from Histopathological Image based on Deep Learning

Face alignment, which is an important step in face recognition, face beautification and many other face-related visual tasks, is to accurately locate the key points with semantic features in the face image. Explicit shape regression is a representative effective face alignment algorithm based on cascade regression, which can deal with lots of complex cases, but still has some shortcomings. An improved ESR algorithm is proposed in this paper, which consists of two parts. In one part, the first derivative of Gaussian filter-based gradient difference features is extracted to represent the facial appearance, and a random regression forest is learned to predict initial face shapes. In the other part, the predicted initial shape is taken as the initial shape of the ESR algorithm. Experiments show that the improved ESR algorithm has higher accuracy than the original ESR algorithm.

Improved explicit shape regression face alignment algorithm

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Breast Cancer Diagnosis from Histopathological Image based on Deep Learning

Improved explicit shape regression face alignment algorithm