Accurate Prediction of Neoadjuvant Chemotherapy Pathological Complete Remission (pCR) for the Four Sub-Types of Breast Cancer

Feng, Xin; Song, Lelian; Wang, Shaofei; Song, Haoqiu; Chen, Hang; Liu, Yuxuan; Lou, Chenwei; Zhao, Jian; Liu, Quewang; Liu, Yang; Zhao, Ruxing; Xing, Kai; Li, Sijie; Yu, Yunhe; Liu, Zhenyu; Yin, Chengyang; Han, Bing; Du, Ye; Xin, Ruihao; Huang, Lan; Fan, Zhimin; Zhou, Fengfeng

doi:10.1109/access.2019.2941543

Cited by 33 publications

(12 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At present, the classification methods based on mammography images are demonstrated as follows: (1) Feng et al proposed a method for detecting lesions based on region growth [ 4 ]; (2) Hmaidan et al used Z-moments as the shape which is one of the methods of a shape descriptor [ 5 ]; (3) Orel et al adopted the salient index number to represent the geometry of the lesion boundary [ 6 ]; (4) Burriel et al adopted the strategy of convolutional neural network (CNN) segmentation in the feature extraction stage [ 7 ]; and (5) detection and classification by wavelet transform were proposed by researchers and highlight the different methods of mammography.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification

Mann¹,

Bindal²,

Balyan³

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification

Mann¹,

Bindal²,

Balyan³

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Kanker payudara adalah kanker yang berkembang dari jaringan payudara, tandatanda kanker payudara dapat berupa benjolan pada payudara, perubahan bentuk payudara, lesung kulit, cairan yang keluar dari puting, atau bercak merah bersisik pada kulit. Pada mereka dengan penyebaran penyakit yang jauh, mungkin ada nyeri tulang, pembengkakan kelenjar getah bening, sesak napas, atau kulit menguning (Feng et al, 2019).…”

Section: Landasan Teori 21 Kanker Payudaraunclassified

Deteksi Dini Kanker Payudara Menggunakan Metode Convolution Neural Network (CNN)

Lestandy

2022

j. inspir.

View full text Add to dashboard Cite

Kanker payudara merupakan penyakit yang mematikan, tidak hanya terjadi pada wanita namun pria juga dapat terkena penyakit ini. Seseorang bisa rentan terkena penyakit kanker payudara seiring bertambahnya usia, baik disebabkan karena pola hidup yang kurang sehat atau bahkan karena keturunan. Oleh karenanya, perlu ada pencegahan sejak dini dengan cara mendeteksi adanya kanker. Deep learning telah menjadi salah satu topik hangat dalam dunia machine learning karena kapabilitasnya yang signifikan dalam memodelkan berbagai data kompleks seperti citra dan suara. Deep learning yang saat ini memiliki hasil paling signifikan dalam pengenalan citra adalah Convolutional Neural Network (CNN). Pada penelitian ini dilakukan deteksi kanker payudara menggunakan metode CNN. Data yang digunakan dalam penelitian ini adalah data citra histopatologi sebanyak 277.524 data dengan komposisi data training 80% dan data uji 20%. Dataset terdiri dari citra hispatologi kanker payudara normal dan tidak normal dimana data dilakukan preprosessing terlebih dahulu. Hasil penelitian ini mendapat akurasi sebesar 80%.

show abstract

“…In the simulation analysis, the experimentation was analyzed on ninety women who have breast cancer. In 2019, XIN FENG et al [2], examined the prospect of PCR prediction by exploiting merely the initial three treatments nodal sizes. Optimal feature integration for every breast cancer subkind was examined from real nodal sizes of the initial three treatments and nodal sizes of subsequent three treatments forecasted from those of 1 st three ones.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, at the time of the treatment there may be no obvious minimization of huge size, still in scenario of positively RTs [2]. Now and then, the therapy might outcome in tumor fragmentation creating the evaluation of mass challenges.…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Controller of Drug Dosage for Cancer Chemotherapy

Gali¹

2021

JCMPS

View full text Add to dashboard Cite

One of the dangerous death-causing diseases called cancer can be cured by exploiting a renowned technique called chemotherapy. Nowadays, to find the optimal drug rate injection the controller-based technique is exploited therefore, the treatment has increased sufficiently. In this paper, a new robust controller which influences the dosage of drug besides the parameter estimation is presented in this paper. A new adaptive square-root unscented Kalman filter (ASr-UKF)is developed in this work. In reality, by exploiting the existing difference function, the error is calculated and it is deployed for the updating procedure of the proposed method in the conventional methods. On the basis of the prior error function, the updating process is performed while scaled to a nonlinear environment. Moreover, to update the process, the scaling factor is developed that includes enhancement in the historical error. At last, the developed controller performance is considered with the conventional methods that entail the suitable effect of drug dosage injection on normal, tumor and immune cells. In addition, it is seen that the adopted model possesses the capability to calculate tumor cells by means ofa superior accuracy rate

show abstract

Accurate Prediction of Neoadjuvant Chemotherapy Pathological Complete Remission (pCR) for the Four Sub-Types of Breast Cancer

Cited by 33 publications

References 65 publications

Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification

Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification

Deteksi Dini Kanker Payudara Menggunakan Metode Convolution Neural Network (CNN)

A Novel Robust Controller of Drug Dosage for Cancer Chemotherapy

Contact Info

Product

Resources

About