Table 1.

Histopathological studies summarized

Pre-processing	Size of images	Model used	Novel technique	Performance	Dataset used	Ref., year
Reinhard et al. method RGB channel processing [50]	128 $\times$ 128, 512 $\times$ 512	CNN from scratch	Patch sampling strategy using CNN and K-means	Accuracy (acc) 88.89 $\%$ overall	Bioimaging 2015 breast histology classification challenge	[31], 2019
	2040 $\times$ 1536	SVM with Xception, ResNet, NasNet, VGG19 architectures	Feature extraction using phylogenetic diverse indexes, and classification of normal, abnormal, benign, malignant, in-situ	Acc (NasNet for classifying normal vs abnormal) 75 $\%$, precision 56 $\%$, acc (VGG19 for classifying benign vs malignant) 72 $\%$, precision 51 $\%$	BACH 2018	[47], 2020
Image resizing, zooming, rotation, random flip, vertical flip	230 $\times$ 230, 460 $\times$ 460	ResNet34, ResNet50	Modified ResNet for classification	Acc 99.75 $\%$, precision 99.18 $\%$, recall 99.37 $\%$	BreakHis	[49], 2021
Cropping, rotation, flip	512 $\times$ 512, 80 $\times$ 80	TL-Mit-Seg	Hybrid-CNN with transferred weights for classification of mitosis	Precision 0.772, recall 0.663, F-measure 0.713	TUPAC16, MITOS12, MITOS14	[41], 2019
Translation, scaling, rotation, flipping, bilinear interpolation (image resized), stain normalization	224 $\times$ 224	AlexNet	Customized CNN to extract deep features for benign and malignant classification	Acc 95 $\%$, SN 97 $\%$, SP 90 $\%$, AUC 99.36 $\%$	BreakHis	[27]. 2021
Image normalization, rotation, reflection	64 $\times$ 64	CNN + LSTM	Combination of CNN and LSTM for classification and nuclear atypic grading	Acc 86.67 $\%$, SP 0.9278, F-Score 0.8663	MITOS-ATYPIA-14	[26], 2021
Image normalization [33]	512 $\times$ 512	AlexNet	Custom CNN classifier for label refiner, tissue-level mitotic region selection, blob analysis and cell level refinement	F-Score 0.75, recall 0.76, precision 0.71	TUPAC16, MITOS12, MITOS14	[28], 2021
Python/keras. preprocessing library	144 $\times$ 96	ResNet50	Pre-trained ResNet50 to classify benign and malignant tissue	Acc 99.10 $\%$	BreakHis	[51], 2020
Patches, image normalization, rotation, flip	224 $\times$ 224	Pre-trained DenseNet	finetuned DenseNet-121 to classify lymph node metastasis	Acc 97.96 $\%$, AUC 99.68 $\%$, SN 97.29 $\%$, SP 97.65 $\%$	BreakHis	[46], 2021
Flip, rotation, contrast enhancement	224 $\times$ 224, 227 $\times$ 227	BreastNet	BreasNet using hypercolumn technique, convolutional, pooling, residual and dense blocks for classification	Acc 98.80 $\%$, F-Score 98.59 $\%$	BreakHis	[37], 2020