Advanced Deep Learning Fusion Model for Early Multi-Classifcation of Lung and Colon Cancer Using Histopathological Images

Background: In recent years, the healthcare feld has experienced signifcant advancements.
New diagnostic techniques, treatments, and insights into the causes of various diseases have emerged.
Despite these progressions, cancer remains a major concern. It is a widespread illness affecting
individuals of all ages and leads to one out of every six deaths. Lung and colon cancer alone account
for nearly two million fatalities. Though it is rare for lung and colon cancers to co-occur, the spread of
cancer cells between these two areas—known as metastasis—is notably high. Early detection of cancer
greatly increases survival rates. Currently, histopathological image (HI) diagnosis and appropriate
treatment are key methods for reducing cancer mortality and enhancing survival rates. Digital image
processing (DIP) and deep learning (DL) algorithms can be employed to analyze the HIs of fve
different types of lung and colon tissues. Methods: Therefore, this paper proposes a refned DL model
that integrates feature fusion for the multi-classifcation of lung and colon cancers. The proposed
model incorporates three DL architectures: ResNet-101V2, NASNetMobile, and EffcientNet-B0. Each
model has limitations concerning variations in the shape and texture of input images. To address
this, the proposed model utilizes a concatenate layer to merge the pre-trained individual feature
vectors from ResNet-101V2, NASNetMobile, and EffcientNet-B0 into a single feature vector, which is
then fne-tuned. As a result, the proposed DL model achieves high success in multi-classifcation by
leveraging the strengths of all three models to enhance overall accuracy. This model aims to assist
pathologists in the early detection of lung and colon cancer with reduced effort, time, and cost. The
proposed DL model was evaluated using the LC25000 dataset, which contains colon and lung HIs.
The dataset was pre-processed using resizing and normalization techniques. Results: The model was
tested and compared with recent DL models, achieving impressive results: 99.8% for precision, 99.8%
for recall, 99.8% for F1-score, 99.96% for specifcity, and 99.94% for accuracy. Conclusions: Thus, the
proposed DL model demonstrates exceptional performance across all classifcation categories