Multi-Feature Classification of Breast Cancer Histopathology Images: An Experimental Investigation in Machine Learning and Deep Learning Paradigm

Atrey, Kushangi; Singh, Bikesh Kumar; Bodhey, Narendra Kuber

doi:10.1590/1678-4324-2023220297

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Article - Engineering, Technology and Techniques • Braz. arch. biol. technol. 66 • 2023 • https://doi.org/10.1590/1678-4324-2023220297 copy

Multi-Feature Classification of Breast Cancer Histopathology Images: An Experimental Investigation in Machine Learning and Deep Learning Paradigm

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The existing practice for Breast Cancer (BC) characterization includes histopathological analysis, which is tedious and time-consuming due to massive data analysis. Further, such techniques are subjected to inter-and intra-observer variability due to the non-availability of skilled pathologists, particularly in low resource settings. Thus, we propose a multi-feature classification technique for risk stratification of BC in Histopathology Images (HI) using machine learning strategies and a Long Short-Term Memory (LSTM) based deep learning approach. Experiments are performed on a publicly available HI database from which a total of 658 image features are extracted, while 192 relevant features are obtained after feature selection using genetic algorithm. The highest accuracy of 99.85% using 192 features under the 5-fold data division protocol is obtained with the LSTM approach. The proposed framework for analyzing HI using multiple grayscale and color features showed promising results and can be an effective tool in the histopathology laboratory.

Keywords:
Intelligent laboratory analysis; Breast cancer; Feature fusion; Machine learning; Deep learning

HIGHLIGHTS

• The proposed approach performs the classification of breast tumors in histopathology images.

• The proposed approach evaluates a multi-feature classifier for risk stratification.

• The performance of different classifiers is compared under different data division protocols.

• The highest classification accuracy of 99.85% after feature selection is reported.

Type of Feature	Number of features	Name of various features
FOS	5	V1-V5: Mean, median, skewness, kurtosis, and standard deviation [²⁹29 Singh BK, Verma K, Panigrahi L, Thoke AS. Integrating radiologist feedback with computer aided diagnostic systems for breast cancer risk prediction in ultrasonic images: An experimental investigation in machine learning paradigm. Expert Syst. Appl. 2017 Dec 30;90:209-23. doi: https://doi.org/10.1016/j.eswa.2017.08.020 https://doi.org/10.1016/j.eswa.2017.08.0... ]
SGLDM	26	V6-V31: ASM, contrast, correlation, SOS, IDM, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, and information measures of correlation-2
GLDS	4	V32-V35: Contrast, angular second moment, entropy, and mean
NGTDM	5	V36-V40: Coarseness, contrast, busyness, complexity, and strength
SFM	4	V41-V44: Coarseness, contrast, periodicity, and roughness
TEM	6	V45-V50: LL, EE, SS, LE, ES, and LS
FDTA	4	V51-V54: Hurst coefficient H(k); k=1 to 4
Shape features	3	V55-V57: area, perimeter, and perim^2/area [³³33 Gonzalez RC, Woods RE. Digital Image Processing Using MATLAB, 2nd ed. Prentice Hall, 2010.]
STI	379	V58-V436: Computes Radial features and angular features [³³33 Gonzalez RC, Woods RE. Digital Image Processing Using MATLAB, 2nd ed. Prentice Hall, 2010.]
IMI	7	V437-V443: Computes the moment invariants of the image
SMTI	6	V444-V449: Average gray level, average contrast, measure of smoothness, third moment, measure of uniformity, and entropy
GLRLM	11	V450-V460: SRE, LRE, GLN, RLN, RP, LGRE, HGRE, SRLGE, SRHGE, LRLGE and LRHGE
SFTA	12	V461-V472: SFTA based texture features

S. No.	Type of features	Name of Feature	No. of features (Details)	S. No.	Type of features	Name of Feature	No. of features (Details)
1.	Color moments based features	Mean	3 (meanR, meanG, meanB)	4.	Color moments based features	Skewness	3 (yR, yG, yB)
2.		Standard deviation	3 (stdR, stdG, stdB)	5.	Wavelet transform based features	Mean	87 (mean1 to mean87)
3.		Variance	3 (VR, VG, VB)	6.	Wavelet transform based features	Standard deviation	87 (std1 to std 87)

S.No.	Parameters	Parameter value used	S.No.	Parameters	Parameter value used
1.	Size of population	50	6.	Crossover	Arithmetic
2.	Value of genome length	658	7.	Crossover Probability	0.8
3.	Type of population	Bitstrings	8.	Mutation	Uniform
4.	Fitness function used	k-NN	9.	Mutation Probability	0.1
5.	Number of generations	100	10.	Selection scheme	Tournament of size 2

Selected features
Spatial texture and shape attributes: V1, V4-V6, V10, V13, V15-V18, V20-V23, V32, V36, V38, V40, V47, V58, V64 , V67, V68, V74, V77, V78, V82, V85, V87, V88, V91, V96, V97, V98, V101, V102, V116, V117, V118, V119, V124, V125, V129, V132, V134, V138, V139, V143, V144, V145, V146, V148, V152, V153, V154, V155, V157, V163, V172, V174, V175, V178, V180,V182, V188, V190, V201, V202 ,V204, V206, V208, V214, V219, V220, V222, V224, V229, V235, V236, V240, V246, V247, V249, V253, V255, V257 ,V258, V260, V263, V268, V273, V283, V284, V286, V287 ,V289, V296, V299, V302, V303, V307, V308, V310, V312, V313, V314, V315, V317, V320, V323, V328, V342, V343, V348, V350, V353, V358, V361, V362, V363, V367, V388, V392, V395, V403, V405, V408, V409, V410, V415, V421, V427, V429, V430, V432, V433, V435, V437 ,V443, V444, V445, V461, V463, V464, V467.
Color features based on color moments: Standard deviation- stdR, stdB; Variance- VR
Spectral color features based on wavelet transform-Mean: mean1, mean2, mean4, mean5, mean9, mean11, mean15, mean17, mean19, mean23, mean27, mean30, mean36, mean40, mean41, mean47, mean49, mean53, mean58, mean60, mean64, mean70, mean71, mean73, mean75, mean77, mean86
Spectral color features based on wavelet transform- Standard deviation: std6, std22, std26, std32, std38, std41, std44, std47, std59, std60, std67, std68, std69, std70, std71, std77, std87

Data division	Performance measures (%)	BPANN classifier			Medium Gaussian SVM classifier			Coarse Gaussian SVM classifier
		Features			Features			Features
		472 grayscale features	12 Color Moments	174 WT features	472 grayscale features	12 Color Moments	174 WT features	472 grayscale features	12 Color Moments	174 WT features
Holdout	A	86.02	91.28	88.12	77.56	92.32	86.75	70.48	92.17	86.75
	S_e	92.29	91.93	92.57	97.57	96.24	95.35	100	96.24	95.58
	S_p	73.66	89.69	79.19	34.91	83.96	68.40	7.55	83.49	67.93
	AUC	82.98	90.81	85.88	66.24	90.10	81.88	53.77	89.87	81.75
	MCC	68.07	79.62	72.90	45.31	82.10	68.64	22.94	81.74	68.64
5-fold	A	94.94	97.57	96.03	78.86	93.10	88.19	71.32	93.15	88.14
	S_e	96.57	98.25	97.08	97.74	96.13	95.11	100	95.99	95.18
	S_p	91.47	96.12	93.79	38.76	86.67	73.49	10.39	87.13	73.18
	AUC	94.02	97.19	95.44	68.25	91.40	84.30	55.19	91.56	84.18
	MCC	88.28	94.39	90.94	49.15	84.00	72.21	27.03	84.13	72.09
10-fold	A	98.46	98.17	98.56	79.21	93.85	89.28	71.22	93.85	88.93
	S_e	98.98	98.76	99.27	97.45	96.28	95.33	100	96.06	95.18
	S_p	97.34	96.91	97.04	40.47	88.68	76.43	10.08	89.15	75.66
	AUC	98.16	97.84	98.15	68.96	92.48	85.88	55.04	92.60	85.42
	MCC	96.41	95.79	96.61	49.96	85.76	74.86	26.61	85.78	74.02

Data Division	Perform-ance measures	Classifiers
		BPANN	SVM
		BPANN	Linear SVM	Quadratic SVM	Cubic SVM	Fine Gaussian SVM	Medium Gaussian SVM	Coarse Gaussian SVM
Hold-out	A	93.53	87.80	94.88	94.58	82.08	91.57	77.11
	S_e	95.58	94.91	96.68	94.91	99.34	96.90	98.67
	S_p	89.15	72.64	91.04	93.87	45.28	80.19	31.13
	AUC	92.37	83.78	93.86	94.39	72.31	88.55	64.90
	MCC	85.06	71.22	88.17	87.73	58.40	80.30	44.69
5-fold	A	98.81	87.84	94.14	94.54	82.53	91.71	79.40
	S_e	98.98	94.89	96.35	95.99	99.56	96.50	97.74
	S_p	98.45	72.87	89.46	91.47	46.36	81.55	40.47
	AUC	98.71	83.88	92.90	93.73	72.96	89.02	69.10
	MCC	97.29	71.37	86.46	87.46	59.77	80.67	50.63
10-fold	A	99.26	87.89	94.99	95.09	83.08	92.16	80.89
	S_e	99.27	94.74	97.15	96.64	99.64	96.72	97.74
	S_p	99.23	73.33	90.39	91.78	47.91	82.48	45.12
	AUC	99.25	84.04	93.77	94.21	73.77	89.60	71.43
	MCC	98.35	71.50	88.41	88.68	61.15	81.73	54.56

Classification technique	Performance measures (%)
Classification technique	A	S_e	S_p	AUC	MCC
BPANN	92.18	94.87	86.64	90.75	82.1
Linear SVM	88.10	92.92	77.83	85.38	72.21
Quadratic SVM	94.13	95.80	90.57	93.18	86.47
Cubic SVM	94.58	96.02	91.51	93.76	87.53
Fine Gaussian SVM	93.22	97.57	83.96	90.76	84.23
Medium Gaussian SVM	93.37	97.57	84.43	91.00	84.58
Coarse Gaussian SVM	93.37	97.57	84.43	91.00	84.58
Naïve Bayesian	67.62	88.72	22.64	55.68	14.87
Linear discriminant	65.06	77.43	38.68	58.06	16.78
Quadratic discriminant	64.46	84.51	21.70	53.11	7.63
Logistic regression	85.99	90.49	76.42	83.45	67.51
Fine k-NN	92.92	93.36	91.98	92.67	84.06
Medium k-NN	90.36	94.25	82.08	88.16	77.56
Coarse k-NN	80.12	93.81	50.94	72.37	51.69
Cosine k-NN	87.50	92.70	76.42	84.56	70.75
Cubic k-NN	89.76	94.69	79.25	86.97	76.03
Weighted k-NN	91.87	93.58	88.21	90.90	81.39

Classification technique	Performance measures (%) for 5-fold data division					Performance measures (%) for 10-fold data division
Classification technique	A	S_e	S_p	AUC	MCC	A	S_e	S_p	AUC	MCC
BPANN	98.41	99.05	97.05	98.05	96.32	99.75	99.85	99.53	99.69	99.43
Linear SVM	87.79	94.09	74.42	84.25	71.30	88.24	94.38	75.19	84.79	72.37
Quadratic SVM	93.65	95.77	89.15	92.46	85.34	94.04	95.84	90.23	93.04	86.29
Cubic SVM	94.59	95.84	91.94	93.89	87.60	94.89	96.35	91.78	94.07	88.24
Fine Gaussian SVM	94.64	97.81	87.91	92.86	87.58	95.14	98.03	88.99	93.51	88.74
Medium Gaussian SVM	94.89	97.88	88.53	93.21	88.16	95.14	97.96	89.15	93.55	88.74
Coarse Gaussian SVM	94.99	97.88	88.84	93.36	88.39	95.19	98.03	89.15	93.59	88.85
Naïve Bayesian	67.64	89.20	21.86	55.53	14.82	67.39	89.05	21.40	55.23	13.71
Linear discriminant	68.68	76.64	51.78	64.21	28.32	68.59	76.72	51.32	64.02	27.98
Quadratic discriminant	66.70	84.96	27.91	56.44	15.26	66.25	82.77	31.16	56.97	15.78
Logistic regression	85.71	90.07	76.43	83.25	66.96	86.50	90.73	77.52	84.12	68.77
Fine k-NN	94.00	95.69	90.39	93.04	86.19	94.54	95.77	91.94	93.85	87.49
Medium k-NN	91.61	93.72	87.13	90.43	80.76	91.91	93.87	87.75	90.81	81.45
Coarse k-NN	81.49	95.40	51.94	73.67	55.47	81.99	95.40	53.49	74.44	56.76
Cosine k-NN	90.22	92.34	85.74	89.04	77.67	90.67	92.99	85.74	89.36	78.60
Cubic k-NN	91.22	93.87	85.58	89.73	79.75	91.91	94.23	86.98	90.61	81.38
Weighted k-NN	93.05	96.06	86.67	91.36	83.88	93.65	96.35	87.91	92.13	85.28

Deep learning	Performance measures (%)	All 658 features	Selected 192 features
Holdout	A	99.83	93.25
	S_e	99.85	95.12
	S_p	99.81	95.75
	AUC	99.83	95.43
	MCC	99.62	84.43
5-fold	A	99.70	99.85
	S_e	99.57	99.93
	S_p	100.00	99.69
	AUC	99.78	99.81
	MCC	99.32	99.66
10-fold	A	98.16	84.89
	S_e	97.43	92.72
	S_p	100.00	75.39
	AUC	98.71	84.05
	MCC	95.82	68.21

Author and year	Size of the dataset	Classifier/ Architecture used	Performance Accuracy
Bhowal et al. (2022) [²⁶26 Bhowal P, Sen S, Velasquez JD, Sarkar R. Fuzzy ensemble of deep learning models using choquet fuzzy integral, coalition game and information theory for breast cancer histology classification. Expert Syst. Appl. 2022 Mar 15;190:116167. doi: https://doi.org/10.1016/j.eswa.2021.116167 https://doi.org/10.1016/j.eswa.2021.1161... ]	400	Fuzzy based ensemble of DL models	96% (two class)
Boumaraf et al. (2021) [²⁵25 Boumaraf S, Liu X, Wan Y, Zheng Z, Ferkous C, Ma X, et al. Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation. Diagnostics. 2021 Mar 16;11(3):528. doi: https://doi.org/10.3390/diagnostics11030528 https://doi.org/10.3390/diagnostics11030... ]	7909	VGG-19	98.13% (Binary) 88.95% (Multi-class)
Hameed et al. (2020) [²⁴24 Hameed Z, Zahia S, Garcia-Zapirain B, Javier Aguirre J, María Vanegas A. Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors. 2020 Aug 5;20(16):4373. doi: https://doi.org/10.3390/s20164373 https://doi.org/10.3390/s20164373... ]	544	VGG16 and VGG19	95.29%
Wang et al. (2020) [²²22 Wang P, Song Q, Li Y, Lv S, Wang J, Li L, et al. Cross-task extreme learning machine for breast cancer image classification with deep convolutional features. Biomed Signal Process Control. 2020 Mar 1;57:101789. doi: https://doi.org/10.1016/j.bspc.2019.101789 https://doi.org/10.1016/j.bspc.2019.1017... ]	1125	D²TL and ICELM	98.18% (Normal vs. Malignant)
Beevi et al. (2019) [¹⁹19 Beevi KS, Nair MS, Bindu GR. Automatic mitosis detection in breast histopathology images using convolutional neural network based deep transfer learning. Biocybern Biomed Eng. 2019 Jan 1;39(1):214-23. doi: https://doi.org/10.1016/j.bbe.2018.10.007 https://doi.org/10.1016/j.bbe.2018.10.00... ]	53+45	VGGNet	90%
Jiang et al. (2019) [¹⁸18 Jiang Y, Chen L, Zhang H, Xiao X. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module, PLoS One. 2019 Mar 29;14(3): e0214587. doi: https://doi.org/10.1371/journal.pone.0214587 https://doi.org/10.1371/journal.pone.021... ]	7909	CNN	99.34% (Binary) 93.81% (Multi-class)
Motlagh et al. (2018) [¹¹11 Motlagh NH, Jannesary M, Aboulkheyr H, Khosravi P, Elemento O, Totonchi M, et al. Breast cancer histopathological image classification: A deep learning approach. bioRxiv. 2018;1-8:242818. doi: https://doi.org/10.1101/242818 https://doi.org/10.1101/242818... ]	6,402 + 7909	Inception and ResNet	98.7%
Rahhal and Mahmoud (2018) [¹²12 Rahhal A, Mahmoud M. Breast Cancer Classification in Histopathological Images using Convolutional Neural Network. Int J Adv Comput Sci Appl. 2018;9(3):64-8.]	9109	CNN: VGGm	86.80%
Bardou et al. (2018) [¹³13 Bardou D, Zhang K, Ahmad SM. Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access. 2018 May 1;6:24680-93. doi: 10.1109/ACCESS.2018.2831280 https://doi.org/10.1109/ACCESS.2018.2831... ]	7909	CNN	98.33%
Nahid et al. (2018) [¹⁴14 Nahid AA, Mehrabi MA, Kong Y. Histopathological breast Cancer image classification by deep neural network techniques guided by local clustering. BioMed Res. Int. 2018 Mar 7;1-20. doi: https://doi.org/10.1155/2018/2362108 https://doi.org/10.1155/2018/2362108... ]	7909	CNN, LSTM, and CNN+LSTM	91%
Golatkar et al. (2018) [¹⁵15 Golatkar A, Anand D, Sethi A. Classification of breast cancer histology using deep learning. InInternational Conference Image Analysis and Recognition 2018 Jun 27 (pp. 837-844). Springer, Cham. doi: https://doi.org/10.1007/978-3-319-93000-8_95 https://doi.org/10.1007/978-3-319-93000-... ]	400	Fine-tuning Inception-v3 CNN	93%
Nahid and Kong (2018) [¹⁶16 Nahid AA, Kong Y. Histopathological breast-image classification using local and frequency domains by convolutional neural network. Information. 2018 Jan 16;9(1):19. doi: https://doi.org/10.3390/info9010019 https://doi.org/10.3390/info9010019... ]	7909	CNN	97.19%
Alirezazadeh et al. (2018) [¹⁷17 Alirezazadeh P, Hejrati B, Monsef-Esfahani A, Fathi A. Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images. Biocybern Biomed Eng. 2018 Jan 1;38(3):671-83. doi: https://doi.org/10.1016/j.bbe.2018.04.008 https://doi.org/10.1016/j.bbe.2018.04.00... ]	7909	Unsupervised domain adaptation, representation learning	88.5% (Average)
Wei et al. (2017) [¹1 Wei B, Han Z, He X, Yin Y. Deep learning model based breast cancer histopathological image classification. In 2017 IEEE 2nd international conference on cloud computing and big data analysis (ICCCBDA), 2017 Apr 28 (pp. 348-353). IEEE. doi: 10.1109/ICCCBDA.2017.7951937 https://doi.org/10.1109/ICCCBDA.2017.795... ]	7909	Deep CNN: Softmax	97%
Proposed method	2015	472 grayscale features +BPANN	98.46%
		12 color moments + BPANN	98.17%
		174 wavelet-based features + BPANN	98.56%
		All features combined (658 features) + BPANN	99.26%
		192 features selected using genetic algorithm+ BPANN	99.75%
		All features combined using deep learning (LSTM) (658 features)	99.83%
		With deep learning (LSTM) using 192 selected features	99.85%

Instituto de Tecnologia do Paraná - Tecpar Rua Prof. Algacyr Munhoz Mader, 3775 - CIC, 81350-010 Curitiba PR Brazil, Tel.: +55 41 3316-3052/3054, Fax: +55 41 3346-2872 - Curitiba - PR - Brazil
E-mail: babt@tecpar.br

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[1] *Correspondence: bsingh.bme@nitrr.ac.in; Tel.: +91-9826469522 (B.K.S.)