Hybridized Machine Learning based Fractal Analysis Techniques for Breast Cancer Classification

Swain, Munmun; Kisan, Sumitra; Chatterjee, Jyotir Moy; Supramaniam, Mahadevan; Mohanty, Sachi Nandan; Jhanjhi, N. Z.; Abdullah, Azween

doi:10.14569/ijacsa.2020.0111024

Cited by 18 publications

(5 citation statements)

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“…Breast cancer is a major disease among women between the ages of 59 and 69. They ( 4 ) also showed that finding tiny tumors early improves predictions and reduces death rates significantly. Mammography is a useful screening diagnostic method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Table 1 shows the comparison of previous studies in terms of accuracy and limitations. Previous studies ( 4 , 5 , 7 , 10 – 12 , 14 ) have some limitations like less number of images in the dataset, less accuracy, hand-crafted features required, lack of diverse datasets, no publically available dataset, and an imbalanced number of images in datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because cancer cells vary in size, shape, and location, automatically detecting and localizing cancer cells in BC images are a huge difficulty. Machine learning (ML) ( 4 ) approaches have found widespread use in a variety of domains, including educational prediction, pattern recognition, image editing, feature reduction, defect diagnosis, face identification, micro-expression recognition, NLP, and medical diagnosis. Its greatest potential has been discovered in the diagnosis of breast cancer ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

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Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

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“…In comparison, offline signatures are quite different, collected using pen paper, open space given to the signer, and features collected are static in nature. Considering previous research on the system, online signatures are more accurate than offline (Swain et al, 2020;Saeed et al, 2020). One major factor which affects the total performance is noise.…”

Section: Digital Signature Vs Handwritten Signaturementioning

confidence: 99%

An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

al.¹

2022

Int. j. adv. appl. sci.

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A signature is a handwritten representation that is commonly used to validate and recognize the writer individually. An automated verification system is mandatory to verify the identity. The signature essentially displays a variety of dynamics and the static characteristics differ with time and place. Many scientists have already found different algorithms to boost the signature verification system function extraction point. The paper is aimed at multiplying two different ways to solve the problem in digital, manual, or some other means of verifying signatures. The various characteristics of the signature were found through the most adequately implemented methods of machine learning (support vector and decision tree). In addition, the characteristics were listed after measuring the effects. An experiment was performed in various language databases. More precision was obtained from the feature.

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“…These algorithms are coupled with increased algorithmic sophistication and computing power. These algorithms make melanoma skin cancer detection better since the deep learning algorithms leverage biological structure and a data-centric decision approach (Lakshmanaprabu et al, 2018 ; Swain et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Untangling Classification Methods for Melanoma Skin Cancer

Kumar

Vatsa²

2022

Front. Big Data

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Skin cancer is the most common cancer in the USA, and it is a leading cause of death worldwide. Every year, more than five million patients are newly diagnosed in the USA. The deadliest and most serious form of skin cancer is called melanoma. Skin cancer can affect anyone, regardless of skin color, race, gender, and age. The diagnosis of melanoma has been done by visual examination and manual techniques by skilled doctors. It is a time-consuming process and highly prone to error. The skin images captured by dermoscopy eliminate the surface reflection of skin and give a better visualization of deeper levels of the skin. However, the existence of many artifacts and noise such as hair, veins, and water residue make the lesion images very complex. Due to the complexity of images, the border detection, feature extraction, and classification process are challenging. Without a proper mechanism, it is hard to identify and predict melanoma at an early stage. Therefore, there is a need to provide precise details, identify early skin cancer, and classify skin cancer with appropriate sensitivity and precision. This article aims to review and analyze two deep neural network-based classification algorithms (convolutional neural network, CNN; recurrent neural network, RNN) and a decision tree-based algorithm (XG-Boost) on skin lesion images (ISIC dataset) and find which of these provides the best classification performance metric. Also, the performance of algorithms is compared using six different metrics—loss, accuracy, precision, recall, F1 score, and ROC.

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Hybridized Machine Learning based Fractal Analysis Techniques for Breast Cancer Classification

Cited by 18 publications

References 0 publications

Breast Cancer Detection and Classification Empowered With Transfer Learning

Breast Cancer Detection and Classification Empowered With Transfer Learning

An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

Untangling Classification Methods for Melanoma Skin Cancer

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