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Jurnal Inovasi Teknologi dan Edukasi Teknik
Published by Universitas Negeri Malang
ISSN : -     EISSN : 27977196     DOI : 10.17977
Core Subject : Engineering,
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Jurnal Inovasi Teknologi dan Edukasi Teknik menerbitkan naskah terkait Teknik Sipil, Teknologi Industri, Teknik Mesin, Teknik Elektro, dan Pendidikan Kejuruan. Fokus dan lingkup jurnal meliputi Teknik Sipil, Teknologi Industri, Teknik Mesin, Teknik Elektro, dan Pendidikan Kejuruan
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 1 Documents
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Search results for , issue "vol. 6 no. 7 (2026)" : 1 Documents clear
DEEP LEARNING FOR COLORECTAL CANCER TISSUE CLASSIFICATION FROM HISTOPATHOLOGY IMAGES: CONTROLLED EVALUATION OF EFFICIENTNET-B0, RESNET50, AND DENSENET121 WITH PATIENT-INDEPENDENT EXTERNAL VALIDATION Mohammed S. H. Al-Tamimi
Jurnal Inovasi Teknologi dan Edukasi Teknik Vol. 6 No. 7 (2026)
Publisher : Universitas Ngeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um068.v6.i7.2026.1

Abstract

Histopathological examination of Haematoxylin and Eosin (H&E) stained tissues is the foundation of diagnosing colorectal cancer (CRC). The increasing digitization of pathology has resulted in a surfeit of images. Currently, computational methods to automatically classify histopathological tissues are in their infancy. In this paper, nine-class colorectal tissue classification was evaluated using EfficientNet-B0, ResNet50, and DenseNet121. The classification problem was standardized with respect to data pre-processing, data augmentation, model optimization and evaluation. The classification models were developed using the NCT-CRC-HE-100K dataset (100,000 histopathology image patches) and validated on the independent CRC-VAL-HE-7K dataset (7,180 image patches). The validation was performed using accuracy, macro-average precision, macro-average recall, macro-F1 score, weighted-F1, and class-wise average precision, average recall, average F1 score and average specificity. Class-level analysis revealed that MUS and STR were challenging classes for the studied architectures with DEB resulting in confusion. A qualitative Grad-CAM analysis was performed on representative images to understand the reason for the predicted class. The study revealed that the three CNN architectures were able to generalize well for colorectal histopathology classification. Controlled external evaluation of deep learning models is essential to understand the class-specific reasons for the errors.

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