Medical image based breast cancer diagnosis: State of the art and future directions

Tariq, Mehreen; Iqbal, Sajid; Ayesha, Hareem; Abbas, Ishaq; Ahmad, Khawaja Tehseen; Niazi, Muhammad Farooq Khan

doi:10.1016/j.eswa.2020.114095

Cited by 55 publications

(16 citation statements)

References 140 publications

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“…Preprocessing is a basic stage of most automated CAD systems [ 35 ]. In the preprocessing stage, raw data are processed to normalize the image or to transform the image to a domain where cancer can be easily diagnosed [ 10 ].…”

Section: Histopathology Image Analysis Methodologymentioning

confidence: 99%

“…Table 1 summarizes reviewed papers on prostate cancer detection and diagnosis. One of the main constraints in conventional ML techniques is their training with a limited number of features, which has been overcome in DL techniques where hundreds to thousands of features can be selected from digital images for classification; however, this process requires significant amount of training time [ 35 ]. Some of these problems are solved in ensemble techniques as the feature extraction stage is done using pretrained deep networks and samples classified using conventional ML classifiers [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

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Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Ayyad

Shehata

Shalaby

et al. 2021

Sensors

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Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

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Section: Histopathology Image Analysis Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Ayyad

Shehata

Shalaby

et al. 2021

Sensors

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“…These tissues include fiber-glandular, fatty, and pectoral muscle tissues [ 6 ]. On mammography, abnormal tissues such as lesions, tumors, lumps, masses, or calcifications may be indicators of breast cancer [ 7 , 8 ]. However, there is always the possibility of human error when analyzing and diagnosing breast cancer due to dense breasts and the high variability between patients [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images

Alkhaleefah

Tan

Chang

et al. 2022

Cancers

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Inspired by Connected-UNets, this study proposes a deep learning model, called Connected-SegNets, for breast tumor segmentation from X-ray images. In the proposed model, two SegNet architectures are connected with skip connections between their layers. Moreover, the cross-entropy loss function of the original SegNet has been replaced by the intersection over union (IoU) loss function in order to make the proposed model more robust against noise during the training process. As part of data preprocessing, a histogram equalization technique, called contrast limit adapt histogram equalization (CLAHE), is applied to all datasets to enhance the compressed regions and smooth the distribution of the pixels. Additionally, two image augmentation methods, namely rotation and flipping, are used to increase the amount of training data and to prevent overfitting. The proposed model has been evaluated on two publicly available datasets, specifically INbreast and the curated breast imaging subset of digital database for screening mammography (CBIS-DDSM). The proposed model has also been evaluated using a private dataset obtained from Cheng Hsin General Hospital in Taiwan. The experimental results show that the proposed Connected-SegNets model outperforms the state-of-the-art methods in terms of Dice score and IoU score. The proposed Connected-SegNets produces a maximum Dice score of 96.34% on the INbreast dataset, 92.86% on the CBIS-DDSM dataset, and 92.25% on the private dataset. Furthermore, the experimental results show that the proposed model achieves the highest IoU score of 91.21%, 87.34%, and 83.71% on INbreast, CBIS-DDSM, and the private dataset, respectively.

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“…In particular, we use VGG-Net (Simonyan & Zisserman, 2015) for the target classification tasks and U-Net (Ronneberger, Fischer, & Brox, 2015) for the multimodal reconstruction pre-training. Both VGG-Net and U-Net represent well-proven network architectures for image-level and pixellevel prediction tasks, respectively (Houssein et al, 2020;Tariq et al, 2020). Additionally, both networks share numerous characteristics due to the fact that the U-Net layers are precisely based on the design of VGG-Net.…”

Section: Network Architecturementioning

confidence: 99%