An effective tumor detection in MR brain images based on deep CNN approach: i-YOLOV5

Figures & data

Table 1. Analysis of YOLOV5 about related studies.

Methodology and Algorithms	Findings	Advantages of the Proposed YOLOV5 Model
826 test datasets applied to Object detection using Remote sensing images collected from GF-1 and GF-2 satellites; 275 Images used for the study; Input Pixels of [300×300, 416×416], [500×500] [800×800], [1000×1000]; Methodology implemented Faster R-CNN, YOLO 3, Single Shot Multi-Box Detector (SSMBD) ([Li et al. 2020])	When contrasted to SSD and Faster R-CNN methods, YOLO3 outperforms in terms of mAP and FPS	It performs much faster than other networks while requiring much less computational power, as demonstrated by its similarity to other models in terms of performance [Table 1]. Training with more than 1500 images per class and more than 10,000 instances per class is proposed to achieve a i-YOLOV5 model. Using up to 10% background images is also proposed to cut down on FPR. In the taxonomy of MRI-BT, greater precision was achieved by CNN than the accuracy of 90.24% obtained by benign or malicious scans.
218 test dataset used from UAV; Input size is 52; Images Pixel of [600×600] to [1024×1024]; Technique used Faster R-CNN; YOLO3 [Benjdira, B. et al. 2019,]	Compared to Faster R-CNN, YOLO3 succeeds in an improved F1 score and shape rate
224 dataset for testing purposes from Google Earth and DOTA; 56 of Input; Pixel of [600×600] [1500×1500]; Technique used for SSMBD, Faster R-CNN, YOLO3 [Zhao K, Ren X 2019]	The mAP and FPS for YOLO3 are superior compared to Faster R-CNN and SSD
Test dataset of 2260 considered from Korea Expressway; Image count 568; Pixel of 600×600; Methodology used YOLO4, SSD Faster, R-CNN ([Kim, Sung, and Park 2020])	SSD has a faster detection speed, while YOLO4 has greater accuracy
Sample dataset of 800 from Custom Refrigerator for training purpose; Input of 70 images; Pixel of [800×800] for images; Method used for Mask RCNN; YOLO3 ([Dorrer and Tolmacheva 2020])	However, the accuracy of the Mask RCNN was more significant based on the assumption that the detection of YOLO3 was higher
Test image of 5939 considered from Custom Electrical dataset; input images of 1400; Resolution of images [600×600]; Technique used : YOLO4; YOLOV5 ([Rahman et al. 2021])	When compared to YOLOV5 machine learning, YOLO4 has a higher mAP value
Training dataset of 1,18000 from MS COCO, images 5000; image pixel is 1500×1500; Method used YOLO3, YOLO4 [Long et al., 2020]	When compared to YOLO3, the mAP of YOLO4 is dramatically higher
Training dataset of 1,18000 from MS COCO, images used:5000; image pixel is 1500×1500; Method:YOLO3, YOLO4 ([Bochkovskiy 2020])	When compared to YOLO3, YOLO4 demonstrates better results in terms of both mAP and fps
Training dataset of 1,18,000 from MS COCO; 5000 images are used for texting purpose; image pixel size is 1024×1024; Method used YOLO3, YOLO4 [Ge et al., 2021]	FPS is higher in YOLO3 than it is in YOLO4 and YOLOV5

Figure 1. A Proposed i-YOLOV5 Model for BT Detection to Feature Extraction.

Figure 2. Enhancement approach of Mosaic Image Processing.

Figure 3. Bounding Box Prediction with tumor specifications.

Figure 4. Ground Truth Test for Brain Tumor Detection.

Figure 5. The proposed i-YOLOV5 model for Brain Tumor Detection.

Table 2. Optimal Hyper Parameters of proposed YOLOV5.

Parameter	Threshold
Epochs	30
Batch-size	15
Rate of Learning	0.0013
Momentum	0.981
Optimizer	SGD

Figure 6. Segmented Lesion Zone of Input Image.

Figure 7. Quantitative measurements of the optimization method.

Table 3. Recognition Speed at Different Resolutions.

Model	Recognition Image Resolution				Network Weights/pcs	Network Weight Size/MB
	1080p	720p	480p	360p
Proposed i-YOLOV5	2.05	2.29	2.51	2.65	76,918,177	344.18

Figure 8. Confusion matrix of comparison ML techniques.

Figure 9. Performance Measures on Multiple Frequency Bands.

Table 4. ML Algorithm comparison using F1-score and Accuracy.

Classifier	ANN	SVM	KNN	i-YOLO v5
Accuracy	88.19	89.92	89.96	91.19
F1-score	91.11	91.06	91.18	91.26

Figure 10. Grid Output for Convolutional Layer Activations for Classification.

Table 5. Performance Metrics of Image Quality (MSE, PSNR, SSIM, FSIM).

Image	Noise Level	PSNR	MSE	SSIM	FSIM
Lena	0.31	22.67	22.34	0.79	0.891
	0.46	17.98	17.95	0.75	0.873
	0.67	15.21	15.24	0.71	0.812
Barbara	0.34	22.98	22.99	0.81	0.891
	0.47	18.89	18.91	0.76	0.858
	0.68	16.59	16.67	0.72	0.815
Cameramen	0.33	22.89	22.91	0.74	0.852
	0.48	19.29	19.31	0.77	0.861
	0.69	17.28	18.21	0.78	0.881

Table 6. Performance measure of MSE, PSNR, SSIM, FSIM, CPU Time.

Images	PSNR	MSE	SSIM	FSIM	CPU Time
Img015	25.01	257.18	0.852	0.841	1.791
Img022	26.19	182.19	0.869	0.853	1.836
Img034	27.28	159.29	0.878	0.862	1.879

Figure 11. The performance measures of mAps of i-YOLOV5 iterations.

Table 7. Learning models of the CNN Models.

Classification	Type	Each Type	Training	Validation	Test
Iter_1	No Tumor	360	377	89	89
Iter_1	Tumor	240
Iter_2	Glioma	110	340	78	78
Iter_2	Meningioma	100
Iter_2	Metastatic	105
Iter_2	Normal	80
Iter_2	Pituitary	105
Iter_3	Score I	160	335	89	89
Iter_3	Score II	180
Iter_3	Score III	160

Figure 12. Localization Results of Input Images with Localization Values.

Figure 13. Loss and Accuracy graph of BT detection.

Table 8. Localization of Proposed Method.

Datasets	mAP	IoU
500	1.01	1.00

Figure 14. ROC Curve for proposed i- YOLOV5.

Figure 15. Confusion Matrix of BT classification using proposed model.

Table 9. Analysis of Accuracy metrics of TP, TN, FP, FN, Accuracy, Specificity, Sensitivity, and Precision.

Performance Measures	Classes	True Positive	True Negative	False Positive	False Negative	Accuracy (%)	Specificity	Sensitivity	Precision
Iter_1	No Tumor	271	329	5	1	99.12	0.989	0.9912	0.9891
Iter_1	Tumor	325	275	1	5	99.23	0.999	0.9891	0.9918
Iter_2	Glioma	184	600	9	10	98.28	0.9878	0.9577	0.9481
Iter_2	Meningioma	132	650	10	11	98.67	0.9867	0.9561	0.9341
Iter_2	Metastatic	139	645	7	15	98.48	0.9989	0.9182	0.9671
Iter_2	Normal	160	599	26	12	96.97	0.9718	0.9381	0.8919
Iter_2	Pituitary	129	645	11	15	97.89	0.9819	0.8919	0.9012
Iter_3	Score I	330	575	13	8	99.29	0.9839	0.9791	0.9718
Iter_3	Score II	249	675	2	1	99.991	0.9998	0.9919	0.9929
Iter_3	Score III	329	578	8	11	99.17	0.9987	0.9829	0.989

Figure 16. Confusion Matrix of BT classification using proposed model.

Figure 17. Performance measure of Proposed model vs other models.

Table 10. Comparison of classification performance between proposed and existing state-of-the-art techniques.

Models	Classification
	Accuracy	AUC
AlexNet	88.13	0.8918
ResNet50	92.19	0.9318
Inception3	85.27	0.8819
GoogleNet	73.41	0.8129
VGG16	87.19	0.9103
Proposed i-YOLOV5	99.32	0.9918

Table 11. Proposed (i-YOLOV5) BT detection with different losses on test datasets.

Method	Loss_Class	Loss_Configuration	Loss_IoU	Loss_Total	mAp
BC-EL + IoU Loss	0.31023	0.68272	0.95657	2.4759	0.82812
Focal Loss + IoU Loss	0.29281	0.28278	0.76373	1.59396	0.88292
Focal Loss + D-IoU Loss	0.32918	0.70182	0.48373	2.18387	0.9024

Table 12. The i-YOLOV5 is compared to other models by (Bernal et al. 2019).

Model	mAP
YOLOV5 (Bernal et al. 2019)	88.28%
i-YOLOV5 (Proposed)	90.24%

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