Table 5. AI models to identify tumor types from MRI images (N=10) with 21 entries.

Remzan 2024 [25], Asiri 2023 [34], Hammad 2023 [39], Rethemiotaki 2023 [45], Mohsen 2023 [55], Asiri 2022 [74], Banik 2023 [47], Saeidifar 2021 [79], Kumar 2022 [62], Isselmou 2020 [86].

LGG: low-grade glioma; HGG: high-grade glioma; CNN: convolutional neural network

ID	Study ID	Study	AI methods	Accuracy	N	Precision	Recall	F1 score	Tumor type
1	1	Remzan 2024 A	CNN	0.95	1621	0.95	0.973	0.9614	Glioma
2	1	Remzan 2024 B	CNN	0.96	1645	0.974	0.939	0.9562	Meningioma
3	1	Remzan 2024 C	CNN	0.99	1757	0.983	0.997	0.99	Pituitary
4	2	Asiri 2023 A	Ensemble algorithms	0.9802	221	0.99	0.99	0.99	Glioma
5	2	Asiri 2023 B	Ensemble algorithms	0.9432	216	0.99	0.95	0.97	Meningioma
6	2	Asiri 2023 C	Ensemble algorithms	0.99	255	1	0.99	0.99	Pituitary
7	3	Hammad 2023 A	CNN	0.9143	1062	0.96	0.91	0.94	Meningioma
8	3	Hammad 2023 B	CNN	0.978	2139	0.96	0.98	0.97	Glioma
9	3	Hammad 2023 C	CNN	0.9956	1395	0.98	1	0.99	Pituitary
10	4	Rethemiotaki 2023 A	CNN	0.9505	926	0.97	0.95	0.99	Glioma
11	4	Rethemiotaki 2023 B	CNN	0.97	937	0.98	0.97	0.99	Meningioma
12	4	Rethemiotaki 2023 C	CNN	0.99	901	0.98	0.99	1	Pituitary
13	5	Mohsen 2023 A	CNN	0.9178	900	0.944	0.973	0.9584	Meningioma
14	5	Mohsen 2023 B	CNN	0.9202	900	0.9725	0.942	0.9571	Pituitary
15	6	Asiri 2022 A	CNN	0.99	826	0.99	0.99	0.99	Glioma
16	6	Asiri 2022 B	CNN	0.95	822	0.93	0.95	0.9399	Meningioma
17	6	Asiri 2022 C	CNN	0.99	395	0.92	0.99	0.9537	Pituitary
18	7	Banik 2023	CNN	0.9866	3000	0.98	0.967	0.9769	Tumor and not tumor
19	8	Saeidifar 2021	Ensemble algorithms	0.995	150	0.93	0.919	0.9262	Tumor and no tumor
20	9	Kumar 2022	Ensemble algorithms	0.977	8000	0.9812	0.963	0.94	Gliomas
21	10	Isselmou 2020	CNN	0.98	250	0.9396	0.988	0.9632	HGG and LGG