Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Faruqui, Nuruzzaman; Yousuf, Mohammad Abu; Kateb, Faris A.; Abdul Hamid, Md.; Monowar, Muhammad Mostafa

doi:10.1016/j.heliyon.2023.e21520

Cited by 24 publications

(3 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…23,24 The advancements achieved in image recognition through deep learning (DL) techniques hold promise for global healthcare by facilitating the identification, diagnosis and prognosis of CRC. [25][26][27] The utilization of DL-trained convolutional neural networks (CNNs) with its application in various disciplines especially in medical image processing has proved to be a spectacular achievement. 28,29 Continuous efforts are being made to create a new CNN model or improve the existing CNN structure to increase its effectiveness in tasks pertaining to computer vision tasks.…”

Section: Related Workmentioning

confidence: 99%

“…The quality of the WCE still frames may be influenced by several factors such as non‐uniform lighting, camera resolution, overexposure and the camera's field of view (FOV) 23,24 . The advancements achieved in image recognition through deep learning (DL) techniques hold promise for global healthcare by facilitating the identification, diagnosis and prognosis of CRC 25–27 . The utilization of DL‐trained convolutional neural networks (CNNs) with its application in various disciplines especially in medical image processing has proved to be a spectacular achievement 28,29 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and development of artificial intelligence‐based application programming interface for early detection and diagnosis of colorectal cancer from wireless capsule endoscopy images

Selvaraj,

Jayanthy

2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

The colorectal cancer (CRC) is gaining attention in the context of gastrointestinal tract diseases as it ranks third among the most prevalent type of cancer. The early diagnosis of the CRC can be done by periodic examination of the colon and rectum for innocuous tissue abnormality called polyp as it has the potential to evolve as malignant in future. The CRC diagnosis using wireless capsule endoscopy requires the dedicated commitment of the medical expert demanding significant time, focus and effort. The accuracy of manual analysis in identifying polyps is extensively reliant on the cognitive condition of the physician, thus emphasizing the requirement for automatic polyp identification. The artificial intelligence integrated computer‐aided diagnosis system could assist the clinician in better diagnosis, thereby reducing the miss‐rates of polyps. In our proposed study, we developed an application program interface to aid the clinician in automatic segmentation of the polyp and evaluation of its dimension by manual placement of four landmarks on the predicted polyp. The segmentation is performed by the proposed light weight Padded U‐Net for the effective polyp segmentation in the colorectal images. We trained and validated the Padded U‐Net with augmented images of Kvasir dataset and calculated the performance parameters. In order to facilitate image augmentation, a graphical user interface called Augment Tree was developed, which incorporates 92 augmentation techniques. The accuracy, recall, precision, IoU, F1‐score, loss achieved during validation of Padded U‐Net were 95.6%, 0.946%, 0.985%, 0.933%, 0.965% and 0.080% respectively. We demonstrated that accuracy was improved and loss was reduced when the model was trained with augmented images rather than only the limited original dataset images. On comparison of our Padded U‐net architecture with recently developed architectures, our model attained optimal performance in all the metrics except accuracy in which it attained marginal performance to the highest value.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and development of artificial intelligence‐based application programming interface for early detection and diagnosis of colorectal cancer from wireless capsule endoscopy images

Selvaraj,

Jayanthy

2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…If the value is larger, it is evident that the two variables are more dependent. A smaller value of χ 2 represents the weak association between two variables [35]. Features that exhibit strong associations with the target variables have been used in this paper.…”

Section: Feature Selectionmentioning

confidence: 99%

A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes

Alazab,

Awajan,

Alazzam

et al. 2024

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These eye-catching figures have made it an attractive playground for cybercriminals. IoT devices are built using resource-constrained architecture to offer compact sizes and competitive prices. As a result, integrating sophisticated cybersecurity features is beyond the scope of the computational capabilities of IoT. All of these have contributed to a surge in IoT intrusion. This paper presents an LSTM-based Intrusion Detection System (IDS) with a Dynamic Access Control (DAC) algorithm that not only detects but also defends against intrusion. This novel approach has achieved an impressive 97.16% validation accuracy. Unlike most of the IDSs, the model of the proposed IDS has been selected and optimized through mathematical analysis. Additionally, it boasts the ability to identify a wider range of threats (14 to be exact) compared to other IDS solutions, translating to enhanced security. Furthermore, it has been fine-tuned to strike a balance between accurately flagging threats and minimizing false alarms. Its impressive performance metrics (precision, recall, and F1 score all hovering around 97%) showcase the potential of this innovative IDS to elevate IoT security. The proposed IDS boasts an impressive detection rate, exceeding 98%. This high accuracy instills confidence in its reliability. Furthermore, its lightning-fast response time, averaging under 1.2 s, positions it among the fastest intrusion detection systems available.

show abstract

Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Das,

Mazumdar,

Khondakar

et al. 2024

BMEMat

View full text Add to dashboard Cite

Machine learning (ML) and nanotechnology interfacing are exploring opportunities for cancer treatment strategies. To improve cancer therapy, this article investigates the synergistic combination of Graphene Oxide (GO)‐based devices with ML techniques. The production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the investigation. GO is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal chemicals. Then, complicated biological data are analyzed using ML algorithms, which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular characteristics. The study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using ML. Predictive modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery systems. Furthermore, tracking treatment outcomes in real time is made possible by ML algorithms, which permit adaptive modifications to therapy regimens. By optimizing medication doses and delivery settings, the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.

show abstract

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Cited by 24 publications

References 66 publications

Design and development of artificial intelligence‐based application programming interface for early detection and diagnosis of colorectal cancer from wireless capsule endoscopy images

Design and development of artificial intelligence‐based application programming interface for early detection and diagnosis of colorectal cancer from wireless capsule endoscopy images

A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes

Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Contact Info

Product

Resources

About