Breast Cancer Detection Using Deep Learning and Machine Learning: A Comparative Analysis

Sharma, Ashok; Kochar, Barjesh; Joshi, Nisheeth; Kumar, Vinay

doi:10.1007/978-981-15-5113-0_39

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…Makalah (Silva, Jesús, 2019) membahas integrasi teknik klasifikasi data mining dan pembelajaran ensemble untuk memprediksi jenis kekambuhan kanker payudara. Makalah (Sharma, Alpna, 2021) telah melakukan upaya untuk mendeteksi kanker payudara menggunakan jaringan saraf dalam menggunakan dataset kanker payudara Wisconsin. Studi (Nguyen, Cuong, Yong Wang, 2013) bertujuan untuk mendiagnosis dan prognostik kanker payudara menggunakan metode pembelajaran mesin berdasarkan pengklasifikasi hutan acak dan teknik pemilihan fitur.…”

Section: Pendahuluanunclassified

Teknik Smote Dan Gini Score Dalam Klasifikasi Kanker Payudara

Ramadhan¹,

Adhinata²

2021

RADIAL

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Breast cancer is a malignancy in breast tissue that can originate from the epithelium of the ducts and lobules. WHO says 30% - 50% of cancer cases can be prevented. Breast cancer prevention can be done utilizing screening or early diagnosis. The purpose of the initial diagnosis is that if a lump appears, predictions can be made whether it is classified as malignant or benign. Breast cancer prediction can be done using a dataset containing cancer-related parameters. However, sometimes the dataset used also has problems such as the amount of data is not balanced and the use of irrelevant features. This study aims to improve breast cancer prediction results by balancing the number of data classes and using the rank feature. The method used is SMOTE for imbalanced data and Gini score for rank features. The classification model used is random forest and naïve Bayes. The results obtained by the random forest classification model are superior to Naïve Bayes.

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Section: Pendahuluanunclassified

Teknik Smote Dan Gini Score Dalam Klasifikasi Kanker Payudara

Ramadhan¹,

Adhinata²

2021

RADIAL

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“…At last, in result the attained false positive rate address the poor condition of the patient and follow the doctor suggestions (Latif et al 2020). Moreover, interpretation of a mammogram is more difficult to identify the affection (Sharma et al 2021), because the abnormalities have only happened in a small specific area in the earlier stage that is measured as 0.5% (Hakim and Awale 2020).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Deep Learning Frame Model for an Accurate Segmentation of Cancer Affected Part in Breast

Dewangan

Sahu

Janghel

2023

Preprint

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Breast cancer is one of the primary causes of death occurring in females around the world. So, the recognition and categorization of breast cancer in the initial stage is necessary for helping the patients to have suitable action. In this research, a novel Spider Monkey-based Convolution Model (SMCM) is developed for detecting breast cancer cells in an early stage. Here, breast Magnetic Resonance Imaging (MRI) is utilized as the dataset trained to the system. Moreover, the developed SMCM function is processed on the breast MRI dataset to primarily detect and segment the affected part of breast cancer. Additionally, segmented images are utilized for tracking in the dataset that has identified the possibility of breast cancer. Moreover, the simulation of this approach is done by Python tool and the parameters of the current research work are evaluated with prevailing works. Hence, the outcomes show that the current research model produces improved accuracy for breast cancer segmentation than other models.

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“…Among them applications of deep learning algorithms have shown promising results. In healthcare sector, deep learning [ 8 ] has shown tremendous development in automatic disease prediction such as tuberculosis detection [ 9 , 10 ], cancer detection [ 11 , 12 ], tumor detection [ 13 , 14 ], bone fracture detection [ 15 , 16 ], genome sequence analysis [ 17 – 19 ] etc. In their study, Abbas et al [ 20 ], proposed a model named DeTraC that classifies chest X-ray images.…”

Section: Introductionmentioning

confidence: 99%