Adaptive Dynamic Learning Rate Optimization Technique for Colorectal Cancer Diagnosis Based on Histopathological Image Using EfficientNet-B0 Deep Learning Model

The elevated death rate associated with colorectal cancer (CRC) continues to impact human
life worldwide. It helps prevent disease and extend human life by being detected early. CRC is
frequently diagnosed and detected through histopathological examination. The decision is based on
clinicians’ subjective perceptions and daily image analyses. Histological image (HI) classification
is difficult because HIs contain multiple tissue types and characteristics. Therefore, deep learning
(DL) models are employed to classify different kinds of CRC HIs. Therefore, to increase the efficiency
of the CRC diagnostic procedure from HIs, we propose a fine-tuning model for the CRC diagnosis
process with the EfficientNet-B0 DL model. The proposed model performs a multi-classification for
HIs. It uses an adaptive learning rate (ALR) to overcome the overfitting problem caused by using the
static learning rate (SLR) and to enhance the performance of detecting the CRC. The ALR compares
the training loss value at the beginning of each epoch. If it is smaller, we increase the ALR; if it is
larger, we decrease it. Our proposed model speeds diagnosis, reduces diagnostic costs, and reduces
medical errors; hence, it enhances the diagnostic procedure from the patient’s perspective. We trained
and evaluated the proposed model over the two datasets (NCT-CRC-HE-100K and CRC-VAL-HE-7K).
Normalization and scaling methods were used to pre-process the NCT-CRC-HE-100K dataset. The
EfficientNet-B0 model attained accuracy, sensitivity, specificity, precision, and an F1-score of 99.87%,
99.64%, 99.95%, 99.62%, and 99.63%, respectively when applied to the NCT-CRC-HE-100K dataset.
On the CRC-VAL-HE-7K dataset, the EfficientNet-B0 model achieved 99%, 94.52%, 99.45%, 94.41%,
and 94.36% for accuracy, sensitivity, specificity, precision, and F1-score, respectively. As a result, the
EfficientNet-B0 model outperforms the state of the art in this field.