## 5 Experimental Results  
### Biomedical Data Sets  
There are no fixed definitions of small/midsize data sets that would apply to all fields in data mining. Whether a data set is considered large or small is relative to the task and the publication date of the research. As many deep learning algorithms require large data sets to avoid overfitting, the expectation today is to produce extremely large data sets. We claim that if the data set contains fewer than 1000 images, then it is small, and if the number of images is between 1001 and 10,000, we say that it is midsize. Each of the activation functions detailed in Section 3 is tested on the CNNs using the following fifteen publicly available biomedical data sets:  
1. CH (CHO data set [72]): this is a data set containing 327 fluorescence microscopy images of Chinese hamster ovary cells divided into five classes: antigiantin, Hoechst 33,258 (DNA), antilamp2, antitop4, and antitubulin. 2. HE (2D HeLa data set [72]): this is a balanced data set containing 862 fluorescence microscopy images of HeLa cells stained with various organelle-specific fluorescent dyes. The images are divided into ten classes of organelles: DNA (Nuclei); ER (Endoplasmic reticulum); Giantin, (cis/medial Golgi); GPP130 (cis Golgi); Lamp2 (Lysosomes); Nucleolin (Nucleoli); Actin, TfR (Endosomes); Mitochondria; and Tubulin. 3. RN (RNAi data set [73]): this is a data set of 200 fluorescence microscopy images of fly cells (D. melanogaster) divided into ten classes. Each class contains 1024 \(\times\)1024 TIFF images of phenotypes produced from one of ten knock-down genes, the IDs of which form the class labels. 4. MA (C. elegans Muscle Age data set [73]): this data set is for classifying the age of a nematode given twenty-five images of C. elegans muscles collected at four ages representing the classes. 5. TB (Terminal Bulb Aging data set [73]): this is the companion data set to MA and contains 970 images of C. elegans terminal bulbs collected at seven ages representing the classes. 6. LY (Lymphoma data set [73]): this data set contains 375 images of malignant lymphoma representative of three types: Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). 7. LG (Liver Gender Caloric Restriction (CR) data set [73]): this data set contains 265 images of liver tissue sections from six-month-old male and female mice on a CR diet; the two classes represent the gender of the mice. 8. LA (Liver Aging Ad libitum data set [73]): this data set contains 529 images of liver tissue sections from female mice on an ad libitum diet divided into four classes representing the age of the mice. 9. CO (Colorectal Cancer [74]): this is a Zenodo data set (record: 53169#.WaXjW8 hJaUm) of 5000 histological images (150 x 150 pixels each) of human colorectal cancer divided into eight classes. 10. BGR (Breast Grading Carcinoma [75]): this is a Zenodo data set (record: 834910#.Wp1 bQ-jOWUI) that contains 300 annotated histological images of twenty-one patients with invasive ductal carcinoma of the breast representing three classes/grades 1-3. 11. LAR (Laryngeal data set [76]): this is a Zenodo data set (record: 1003200#.WdeQc-nBxOnQ) containing 1320 images of thirty-three healthy and early-stage cancerous laryngeal tissues representative of four tissue classes. 12. HP (set of immunohistochemistry images from the Human Protein Atlas [77]): this is a Zenodo data set (record: 3875786#.XthkoDozY2w) of 353 images of fourteen proteins in nine normal reproductive tissues belonging to seven subcellular locations. The data set in [77] is partitioned into two folds, one for training (177 images) and one for testing (176 images). * RT (2D 3T3 Randomly CD-Tagged Images: Set 3 [78]): this collection of 304 2D 3T3 randomly CD-tagged images was created by randomly generating CD-tagged cell clones and imaging them by automated microscopy. The images are divided into ten classes. As in [78], the proteins are put into ten folds so that images in the training and testing sets never come from the same protein.