Independent component analysis (ICA) denoising methods can be highly effective for reducing functional magnetic resonance imaging (fMRI) noise. ICA denoising method success heavily depends, however, on the accurate classification of fMRI data ICs as either neural signal or noise. While manual IC classification ("manual ICA denoising") is a current gold-standard, it requires extensive time and training. Automated methods of IC classification ("automated ICA denoising"), meanwhile, are less accurate and effective, especially in clinical populations where motion artifacts are more common. To address these challenges, a novel denoising method, Comprehensive Independent Component Analysis Denoising Assistant (CICADA), was developed. Uniquely, CICADA uses manual classification guidelines to automatically, comprehensively, and accurately capture most common sources of fMRI noise. As such, we hypothesized that CICADA would perform similarly to manual ICA denoising and outperform other current automated denoising methods. CICADA was evaluated against two well-established automated ICA denoising methods (FIX and ICA-AROMA) across three fMRI datasets. The datasets included high-motion resting-state (N = 57) and visual-task data (N = 53), both from individuals with schizophrenia, as well as low-motion resting-state healthy control data from an openly available dataset (N = 56). IC classification accuracy was first evaluated against manual IC classification in a subset (N = 30) of each dataset. Denoising performance efficacy was then evaluated with commonly used quality control (QC) benchmarks and correlations with fMRI noise profiles across all data. With a 97.9% mean overall accuracy in IC classification, CICADA performed nearly as well as manual IC classification and was significantly more accurate than FIX (92.9% mean overall accuracy; all p-values < 0.01) and ICA-AROMA (83.8% mean overall accuracy; all p-values < 0.001). CICADA also matched or outperformed FIX and ICA-AROMA across most QC and noise profile metrics across all data. Furthermore, CICADA greatly eased implementation of manual ICA denoising by decreasing the number of ICs a user must inspect by an average of 75%. Overall, CICADA is a novel, accurate, comprehensive, and automated ICA denoising tool for use in both resting-state and task-based fMRI. It performed similarly to the labor-intensive manual IC classification gold-standard and, in some datasets, outperformed current automated ICA denoising methods. Finally, CICADA may facilitate more efficient manual ICA denoising without reducing efficacy.