Conceptual data sampling for breast cancer histology image classification

Rezk, Eman; Awan, Zainab; Islam, Fahad; Jaoua, Ali; Al-Maadeed, Somaya; Zhang, Nan; Das, Gautam; Rajpoot, Nasir M.

doi:10.1016/j.compbiomed.2017.07.018

Cited by 16 publications

(5 citation statements)

References 32 publications

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“…To pre-process the breast ultrasound images, we first removed irrelevant information, such as manufacturer’s labels, directional markings and text fields, which could interfere with image interpretation and lead to incorrect results during noise reduction. This step ensured a clear and focused image [ 28 ]. Next, the images were normalised and transformed to a consistent size and format for efficient processing by machine learning algorithms.…”

Section: Methodsmentioning

confidence: 99%

Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework

et al. 2023

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We present an analysis and evaluation of breast cancer detection and diagnosis using segmentation models. We used an advanced semantic segmentation method and a deep convolutional neural network to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images. To improve the segmentation results, we used six models to analyse 309 patients, including 151 benign and 158 malignant tumour images. We compared the Unet3+ architecture with several other models, such as FCN, Unet, SegNet, DeeplabV3+ and pspNet. The Unet3+ model is a state-of-the-art, semantic segmentation architecture that showed optimal performance with an average accuracy of 82.53% and an average intersection over union (IU) of 52.57%. The weighted IU was found to be 89.14% with a global accuracy of 90.99%. The application of these types of segmentation models to the detection and diagnosis of breast cancer provides remarkable results. Our proposed method has the potential to provide a more accurate and objective diagnosis of breast cancer, leading to improved patient outcomes.

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Section: Methodsmentioning

confidence: 99%

Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework

et al. 2023

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“…We use it to build features from texts, and to find some significant decreasing importance order of objects or attributes in a binary context. Recently, it has been applied for context reduction without losing implications corresponding to the functional dependencies defined between the attributes of pixels in the image [6] [7].…”

Section: Formal Concept Analysis Backgroundmentioning

confidence: 99%

“…For each random input, the program execution generates a set of traces T r. These traces T r are then mapped to a formal context F C that we include into the global set of patterns in a reduced form (the knowledge K). Implicitly, F C represents the list of functional dependencies between all attributes of the traces generated by one program execution K [6] [7] [8]. In this research, for the learning process, we try to find the minimum set of random inputs after which the gathered knowledge K becomes stable enough to be used for discovering anomalies.…”

Section: Introductionmentioning

confidence: 99%

“…In the following section II, we present the state of the art about anomalies detection in a program and show to what extent it is similar to anomaly detection in data. In section III, we give some background on Formal Concept Analysis (FCA) as an important theoretical foundation for data analysis and for generating functional dependencies from formatted data [6] [7]. In section IV, we explain how we can represent functional dependencies in a formatted relational database with a formal context in reduced form.…”

Section: Introductionmentioning

confidence: 99%

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Anomalies Detection in Software by Conceptual Learning From Normal Executions

et al. 2020

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Could we detect anomalies during the run-time of a program by learning from the analysis of its previous traces for normally completed executions? In this paper we create a featured data set from program traces at run time, either during its regular life, or during its testing phase. This data set represents execution traces of relevant variables including inputs, outputs, intermediate variables, and invariant checks. During a learning mining step, we start from exhaustive random training input sets and map program traces to a minimal set of conceptual patterns. We employ formal concept analysis to do this in an incremental way, and without losing dependencies between data set features. This set of patterns becomes a reference for checking the normality of future program executions as it captures invariant functional dependencies between the variables that need to be preserved during execution. During the learning step, we consider enough input classes corresponding to the different patterns by using random input selection until reaching stability of the set of patterns (i.e. the set is almost no longer changing, and only negligible new patterns are not reducible to it). Experimental results show that the generated patterns are significant in representing normal program executions. They also enable the detection of different executable code contamination at early stages. The proposed method is general and modular. If applied systematically, it enhances software resilience against abnormal and unpredictable events.

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“…In navigating this complex terrain, the existing classification algorithms grapple with singular features-be it spatial, morphological, or textural. The demand echoes for a comprehensive framework adept at handling multiple feature types, bridging the existing gaps and fortifying the foundation for a new era in cancer diagnosis [10]. As the quest for reliable and precise classification intensifies, the intersection of medical expertise and technological innovation emerges as the crucible where breakthroughs are forged and the relentless pursuit of conquering cancer unfolds.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning-based CNN Model for the Classification of Breast Cancer from Histopathological Images

2024

IJACSA

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Breast cancer can have significant emotional and physical repercussions for women and their families. The timely identification of potential breast cancer risks is crucial for prompt medical intervention and support. In this research, we introduce innovative methods for breast cancer detection, employing a Convolutional Neural Network (CNN) architecture and Transfer Learning (TL) technique. Our foundation is the ICAIR dataset, encompassing a diverse array of histopathological images. To harness the capabilities of deep learning and expand the model's knowledge base, we propose a TL model. The CNN component adeptly extracts spatial features from histopathological images, while the TL component incorporates pretrained weights into the model. To tackle challenges arising from limited labeled data and prevent overfitting, we employ ResNet152v2. Utilizing a pre-trained CNN model on extensive image datasets initializes our CNN component, enabling the network to learn pertinent features from histopathological images. The proposed model achieves commendable accuracy (96.47%), precision (96.24%), F1-score (97.18%), and recall (96.63%) in identifying potential breast cancer cases. This approach holds the potential to assist medical professionals in early breast cancer risk assessment and intervention, ultimately enhancing the quality of care for women's health.

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Conceptual data sampling for breast cancer histology image classification

Cited by 16 publications

References 32 publications

Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework

Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework

Anomalies Detection in Software by Conceptual Learning From Normal Executions

Transfer Learning-based CNN Model for the Classification of Breast Cancer from Histopathological Images

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