A computer-implemented method of identifying unreliable clusters to improve accuracy and efficiency of base calling, the computer-implemented method including: accessing per-cycle cluster data for a plurality of clusters and for a first subset of sequencing cycles of a sequencing run; base calling of a multi-pixel image through a neural network-based base caller each cluster in the plurality of clusters at each sequencing cycle in the first subset of sequencing cycles, including: processing the per-cycle cluster data and generating intermediate representations of the per-cycle cluster data through the neural network-based base caller, and processing the intermediate representations through a normalization function of an output layer of the neural network-based base caller and producing a per-cluster, per-cycle probability quadruple for each cluster and for each sequencing cycle, wherein a particular per-cluster, per-cycle probability quadruple identifies probabilities of a base incorporated in a particular cluster at a particular sequencing cycle being A, C, T, and G; using the identified probabilities to determine a filter value as a function of a highest probability that determines a base call for each per-cluster, per-cycle probability quadruple, thereby generating a sequence of filter values for each cluster; identifying as unreliable clusters those clusters in the plurality of clusters whose sequences of filter values contain at least "N" number of filter values below a threshold "M"; and bypassing base calling of the unreliable clusters at a remainder of sequencing cycles of the sequencing run, thereby base calling, at the remainder of sequencing cycles, only those clusters in the plurality of clusters that are not identified as the unreliable clusters. accessing per-cycle cluster data for a plurality of clusters and for a first subset of sequencing cycles of a sequencing run; base calling of a multi-pixel image through a neural network-based base caller each cluster in the plurality of clusters at each sequencing cycle in the first subset of sequencing cycles, including: processing the per-cycle cluster data and generating intermediate representations of the per-cycle cluster data through the neural network-based base caller, and processing the intermediate representations through a normalization function of an output layer of the neural network-based base caller and producing a per-cluster, per-cycle probability quadruple for each cluster and for each sequencing cycle, wherein a particular per-cluster, per-cycle probability quadruple identifies probabilities of a base incorporated in a particular cluster at a particular sequencing cycle being A, C, T, and G; processing the per-cycle cluster data and generating intermediate representations of the per-cycle cluster data through the neural network-based base caller, and processing the intermediate representations through a normalization function of an output layer of the neural network-based base caller and producing a per-cluster, per-cycle probability quadruple for each cluster and for each sequencing cycle, wherein a particular per-cluster, per-cycle probability quadruple identifies probabilities of a base incorporated in a particular cluster at a particular sequencing cycle being A, C, T, and G; using the identified probabilities to determine a filter value as a function of a highest probability that determines a base call for each per-cluster, per-cycle probability quadruple, thereby generating a sequence of filter values for each cluster; identifying as unreliable clusters those clusters in the plurality of clusters whose sequences of filter values contain at least "N" number of filter values below a threshold "M"; and bypassing base calling of the unreliable clusters at a remainder of sequencing cycles of the sequencing run, thereby base calling, at the remainder of sequencing cycles, only those clusters in the plurality of clusters that are not identified as the unreliable clusters.